[1] Grand challenges 1993: High performance computing and communications, 1992. The FY 1992 U.S. Research and Development Program, Committee on Physical, Mathematical, and Engineering Sciences, Federal Coordinating Council for Science, Engineering, and Technology, Office of Science and Technology Policy.
[ bib ]
[2] Applications of Evolutionary Computing, EvoWorkshops 2006, volume 3907 of Lecture Notes in Computer Science. Springer, 2006.
[ bib ]
[3] Agnar Aamodt and Enric Plaza. Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Communications, 7(1):39-59, 1994.
[ bib ]
[4] M. Mar Abad and L. Daniel Hernández. Discretización basada en el principio de minimización estructural vectorial del riesgo. In Proceedings of the CAEPIA2001, pages 283-292, 2001.
[ bib ]
[5] Hussein A. Abbass, Jaume Bacardit, Martin V. Butz, and Xavier Llora. Online adaption in learning classifier systems: Stream data mining. Technical Report 2004031, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 2004.
[ bib ]
[6] H. Abe and N. Go. Noninteracting local-structure model of folding and unfolding transition in globular proteins. ii. application to two-dimensional lattice proteins. Biopolymers, 20(5):1013-1031, 1981.
[ bib ]
[7] R Adamczak, A Porollo, and J Meller. Accurate prediction of solvent accessibility using neural networks based regression. Proteins, 56:753-767, 2004.
[ bib ]
[8] J. Aguilar, J. Bacardit, and F. Divina. Experimental evaluation of discretization schemes for rule induction. In GECCO 2004: Proceedings of the Genetic and Evolutionary Computation Conference, pages 828-839. Springer-Verlag, LNCS 3102, 2004.
[ bib ]
[9] Jesús Aguilar, José Riquelme, and Miguel Toro. Three geometric approaches for representing decision rules in a supervised learning system. In Late Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO'99), pages 8-15, 19919999.
[ bib ]
[10] Jesus S. Aguilar-Ruiz, J. Riquelme, and M. Toro. Data set editing by ordered projection. In Proceedings of the European Conference on Artificial Intelligence, pages 251-255. IOS Press, 2000.
[ bib ]
[11] Jesus S. Aguilar-Ruiz, José C. Riquelme, and Carmelo Del Valle. Improving the evolutionary coding for machine learning tasks. In Proceedings of the European Conference on Artificial Intelligence, ECAI'02, pages pp. 173-177, Lyon, France, 2002. IOS Press.
[ bib ]
[12] J.S. Aguilar-Ruiz, J.C. Riquelme, and M. Toro. Evolutionary learning of hierarchical decision rules. IEEE Transactions on Systems, Man and Cybernetics, Part B, 33(2):324-331, April 2003.
[ bib ]
[13] E. Aguirre, A. González, and Raúl Pérez. Un modelo de selección de características para los algoritmos de aprendizaje basados en el modelo pittsburgh. In ``Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)'', pages 354-360, 2002.
[ bib ]
[14] David W. Aha, Dennis F. Kibler, and Mark K. Albert. Instance-based learning algorithms. Machine Learning, 6(1):37-66, 1991.
[ bib ]
[15] Manu Ahluwalia and Larry Bull. A genetic programming-based classifier system. In Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 11-18. Morgan Kaufmann, 1999.
[ bib ]
[16] S Ahmad, MM Gromiha, and A Sarai. Real value prediction of solvent accessibility from amino acid sequence. Proteins, 50(4):629-635, 2003.
[ bib ]
[17] Satoshi Akanuma, Takanori Kigawa, and Shigeyuki Yokoyama. Combinatorial mutagenesis to restrict amino acid usage in an enzyme to a reduced set. Proc Natl Acad Sci U S A, 99(21):13549-13553, 2002.
[ bib | http ]
[18] J. Alcala-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, and F. Herrera. Keel: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing, 2008.
[ bib ]
[19] Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res, 25:3389-3402, 1997.
[ bib ]
[20] David P. Anderson. BOINC: A system for public-resource computing and storage. In GRID '04: Proceedings of the Fifth IEEE/ACM International Workshop on Grid Computing, pages 4-10, Washington, DC, USA, 2004. IEEE Computer Society.
[ bib ]
[21] Peter J. Angeline. Subtree crossover causes bloat. In Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 745-752. Morgan Kaufmann, 1998.
[ bib ]
[22] Thomas Bäack, David B. Fogel, and Zbigniew Michalewicz, editors. Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol, UK, UK, 1997.
[ bib ]
[23] J. Bacardit. Analysis of the initialization stage of a pittsburgh approach learning classifier system. In GECCO 2005: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1843-1850. ACM Press, 2005.
[ bib ]
[24] J. Bacardit. Analysis of the initialization stage of a pittsburgh approach learning classifier system. In GECCO 2005: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1843-1850. ACM Press, 2005.
[ bib ]
[25] J. Bacardit and J. M. Garrell. Analysis and improvements of the adaptive discretization intervals knowledge representation. In GECCO 2004: Proceedings of the Genetic and Evolutionary Computation Conference, pages 726-738. Springer-Verlag, LNCS 3103, 2004.
[ bib ]
[26] J. Bacardit, D.E. Goldberg, M.V. Butz, X. Llorà, and J. M. Garrell. Speeding-up pittsburgh learning classifier systems: Modeling time and accuracy. In Parallel Problem Solving from Nature - PPSN 2004, pages 1021-1031. Springer-Verlag, LNCS 3242, 2004.
[ bib ]
[27] Jaume Bacardit. Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Spain, 2004.
[ bib ]
[28] Jaume Bacardit, Ester Bernadó-Mansilla, and Martin V. Butz. Learning classifier systems: Looking back and glimpsing ahead. In Learning Classifier Systems. Revised Selected Papers of the International Workshop on Learning Classifier Systems 2006-2007, pages 1-21. LNAI 4998, Springer-Verlag, 2008.
[ bib ]
[29] Jaume Bacardit, Edmund K. Burke, and Natalio Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic Computing, in press, 2009.
[ bib ]
[30] Jaume Bacardit, Edmund K. Burke, and Natalio Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic Computing, in press.
[ bib ]
[31] Jaume Bacardit and Martin V. Butz. Data mining in learning classifier systems: Comparing XCS with GAssist. In Proceedings of the 7th International Workshop on Learning Classifier Systems. (in press), LNAI, Springer-Verlag, 2004.
[ bib ]
[32] Jaume Bacardit and Martin V. Butz. Data mining in learning classifier systems: Comparing XCS with GAssist. In Advances at the frontier of Learning Classifier Systems, volume 4399, pages 282-290. Springer-Verlag, 2007.
[ bib ]
[33] Jaume Bacardit and Josep M. Garrell. Evolution of adaptive discretization intervals for A rule-based genetic learning system. In Proceedings of the Fourth Genetic and Evolutionary Computation Conference, page 677. Morgan Kaufmann Publishers, 2002.
[ bib ]
[34] Jaume Bacardit and Josep M. Garrell. Evolution of multi-adaptive discretization intervals for A rule-based genetic learning system. In Proceedings of the Eighth Iberoamerican Conference on Artificial Intelligence, pages 350-360. LNAI vol. 2527, Springer-Verlag, 2002.
[ bib ]
[35] Jaume Bacardit and Josep M. Garrell. Métodos de generalización para sistemas clasificadores de Pittsburgh. In ``Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)'', pages 486-493, 2002.
[ bib ]
[36] Jaume Bacardit and Josep M. Garrell. The role of interval initialization in a gbml system with rule representation and adaptive discrete intervals. In Proceedings of the Fifth Catalan Conference on Artificial Intelligence, pages 184-195. LNAI vol. 2504, Springer-Verlag, 2002.
[ bib ]
[37] Jaume Bacardit and Josep M. Garrell. Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In Proceedings of the 6th International Workshop on Learning Classifier Systems, 2003.
[ bib ]
[38] Jaume Bacardit and Josep M. Garrell. Comparison of training set reduction techniques for pittsburgh approach genetic classifier systems. In ``X Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA2003)'', volume 1, pages 223-226, 2003.
[ bib ]
[39] Jaume Bacardit and Josep M. Garrell. Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system. In Proceedings of the Genetic and Evolutionary Computation Conference - GECCO2003, pages 1818-1831. LNCS 2724, Springer-Verlag, 2003.
[ bib ]
[40] Jaume Bacardit and Josep M. Garrell. Incremental learning for pittsburgh approach classifier systems. In ``Segundo Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados.'', pages 303-311, 2003.
[ bib ]
[41] Jaume Bacardit and Josep M. Garrell. Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005, pages 59-79. Springer-Verlag, LNCS 4399, 2007.
[ bib ]
[42] Jaume Bacardit, David E. Goldberg, and Martin V. Butz. Improving the performance of a pittsburgh learning classifier system using a default rule. In Proceedings of the 7th International Workshop on Learning Classifier Systems. (in press), LNAI, Springer-Verlag, 2004.
[ bib ]
[43] Jaume Bacardit, David E. Goldberg, and Martin V. Butz. Improving the performance of a pittsburgh learning classifier system using a default rule. In Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005, pages 291-307. Springer-Verlag, LNCS 4399, 2007.
[ bib ]
[44] Jaume Bacardit and Natalio Krasnogor. Biohel: Bioinformatics-oriented hierarchical evolutionary learning. Nottingham eprints, University of Nottingham, 2006.
[ bib ]
[45] Jaume Bacardit and Natalio Krasnogor. Smart crossover operator with multiple parents for a pittsburgh learning classifier system. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1441-1448. ACM Press, 2006.
[ bib ]
[46] Jaume Bacardit and Natalio Krasnogor. Empirical evaluation of ensemble techniques for a pittsburgh learning classifier system. In Learning Classifier Systems, Revised Selected Papers of IWLCS 2006-2007, pages 255-268. LNAI 4998, Springer-Verlag, 2008.
[ bib ]
[47] Jaume Bacardit and Natalio Krasnogor. Fast rule representation for continuous attributes in genetics-based machine learning. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1421-1422. ACM, 2008.
[ bib ]
[48] Jaume Bacardit and Natalio Krasnogor. Performance and efficiency of memetic pittsburgh learning classifier systems. Evolutionary Computation Journal, 17(3):in press, 2009.
[ bib ]
[49] Jaume Bacardit and Natalio Krasnogor. Performance and efficiency of memetic pittsburgh learning classifier systems. Evolutionary Computation Journal, 17(3):in press, 2009.
[ bib ]
[50] Jaume Bacardit, Michael Stout, Jonathan D. Hirst, and Natalio Krasnogor. Data mining in proteomics with learning classifier systems. In L. Bull, E. Bernado Mansilla, and J. Holmes, editors, Learning Classifier Systems in Data Mining, pages 17-46. Springer, 2008.
[ bib ]
[51] Jaume Bacardit, Michael Stout, Jonathan D. Hirst, Kumara Sastry, Xavier Llorà, and Natalio Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 346-353. ACM Press, 2007.
[ bib ]
[52] Jaume Bacardit, Michael Stout, Jonathan D Hirst, Alfonso Valencia, Robert E Smith, and Natalio Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10:6, 2009.
[ bib ]
[53] Jaume Bacardit, Michael Stout, Natalio Krasnogor, Jonathan D. Hirst, and Jacek Blazewicz. Coordination number prediction using learning classifier systems: performance and interpretability. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 247-254. ACM Press, 2006.
[ bib ]
[54] Thomas Bäck. Generalized convergence models for tournament- and (mu, lambda)-selection. In Proceedings of the 6th International Conference on Genetic Algorithms, pages 2-8. Morgan Kaufmann Publishers Inc., 1995.
[ bib ]
[55] Pierre Baldi and Gianluca Pollastri. The principled design of large-scale recursive neural network architectures dag-rnns and the protein structure prediction problem. Journal of Machine Learning Research, 4:575 - 602, 2003.
[ bib ]
[56] George W. Bassel, Pauline Fung, Tsz-fung Freeman Chow, Justin A. Foong, Nicholas J. Provart, and Sean R. Cutler. Elucidating the Germination Transcriptional Program Using Small Molecules. Plant Physiol., 147(1):143-155, 2008.
[ bib | http ]
[57] Jeffrey K. Bassett and Kenneth A. De Jong. Evolving behaviors for cooperating agents. In International Syposium on Methodologies for Intelligent Systems, pages 157-165. Springer-Verlag, 2000.
[ bib | .html ]
[58] David Beasley, David R. Bull, and Ralph R. Martin. An overview of genetic algorithms: Part 2, research topics. University Computing, 15(4):170-181, 1993.
[ bib | .html ]
[59] H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne. The protein data bank. Nucleic Acids Res, 28:235-242, 2000.
[ bib ]
[60] Ester Bernadó and Josep Maria Garrell. Multiobjective learning in a genetic classifier system (MOLeCS). Butlletí de l'Associació Catalana l'Intel.ligència Artificial, 22:102-111, 2000.
[ bib ]
[61] Ester Bernadó and Josep Maria Garrell. Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Special Issue of the Evolutionary Computation Journal on Learning Classifier Systems (in press), 2003.
[ bib ]
[62] Ester Bernadó, Xavier Llorà, and Josep M. Garrell. Anàlisi de l'avaluació incremental i de l'aplicació eficient de l'algorisme genètic en un sistema classificador. In Proceeding of the Fourth Catalan Conference on Artificial Inteligence, 2001.
[ bib ]
[63] Ester Bernadó, Xavier Llorà, and Josep M. Garrell. XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks. In Fourth International Workshop on Learning Classifier Systems - IWLCS-2001, pages 337-341, 2001.
[ bib ]
[64] Ester Bernadó, Abdelouahab Mekaouche, and Josep M. Garrell. A Study of a Genetic Classifier System Based on the Pittsburgh Approach on a Medical Domain. In 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-99, pages 175-184. Springer-Verlag, 1999.
[ bib ]
[65] Ester Bernadó-Mansilla and Josep Maria Garrell-Guiu. Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209-238, 2003.
[ bib | http ]

Keywords: dblp
[66] M.J. Betts and R.B. Russell. Amino acid properties and consequences of subsitutions. In Bioinformatics for Geneticists. Wiley, 2003.
[ bib ]
[67] C. Blake, E. Keogh, and C. Merz. UCI repository of machine learning databases, 1998. (www.ics.uci.edu/mlearn/MLRepository.html).
[ bib ]
[68] Lashon B. Booker. Intelligent Behavior as an Adaptation to the Task Environment. PhD thesis, The University of Michigan, 1982.
[ bib ]
[69] L.B. Booker. Recombination distribution for genetic algorithms. In Foundations of Genetic Algorithms 2, pages 29-44. Morgan Kaufmann, 1993.
[ bib ]
[70] Carl Branden and John Tooze. Introduction to Protein Structure. Garland, second edition, 1999.
[ bib ]

Keywords: protein_structure
[71] L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
[ bib ]
[72] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA, 1984.
[ bib ]
[73] C. Brodley. Addressing the selective superiority problem: Automatic algorithm /model class selection. In Proceedings of the Tenth International Conference on Machine Learning, pages 17-24. Morgan Kaufmann Publishers, 1993.
[ bib ]
[74] B.M. Broome and M.H. Hecht. Nature disfavors sequences of alternating polar and non-polar amino acids: implications for amyloidogenesis. J Mol Biol, 296(4):961-968, 2000.
[ bib ]
[75] Gavin Brown, Tim Kovacs, and James A. R. Marshall. Ucspv: principled voting in ucs rule populations. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1774-1781. ACM Press, 2007.
[ bib ]
[76] Will Browne. The development of an industrial learning classifier system for data-mining in a steel hot strip mill. In Larry Bull, editor, Applications of Learning Classifier Systems, pages 223-259. Springer-Verlag, 2004.
[ bib ]
[77] Will N. Browne and Charalambos Ioannides. Investigating scaling of an abstracted lcs utilising ternary and s-expression alphabets. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2759-2764. ACM Press, 2007.
[ bib ]
[78] L. Bull, J. Hurst, and A. Tomlison. Self-adaptive mutation in classifier system controllers. In J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblatt, and S.W. Wilson, editors, From Animals to Animats 6 - The Sixth International Conference on the Simulation of Adaptive Behaviour. MIT Press, 2000.
[ bib ]
[79] L. Bull, M. Studley, A. Bagnall, and I. Whittley. Learning classifier system ensembles with rule-sharing. Evolutionary Computation, IEEE Transactions on, 11(4):496-502, 2007.
[ bib | http ]

Keywords: ga, lcs
[80] Larry Bull. On lookahead and latent learning in simple lcs. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2633-2636. ACM, 2007.
[ bib ]
[81] Donald S. Burke, Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. Putting more genetics into genetic algorithms. Evolutionary Computation, 6(4):387-410, Winter 1998.
[ bib | .html ]
[82] Martin Butz, Tim Kovacs, Pier Luca Lanzi, and Stewart W. Wilson. Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation, 8(1):28-46, 2004.
[ bib ]
[83] Martin V. Butz. Documentation of XCS+TS c-code 1.2. Technical Report 2003023, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 2003.
[ bib ]
[84] Martin V. Butz. Rule-based Evolutionary Online Learning Systems: Learning Bounds, Classification and Prediction. PhD thesis, University of Illinois at Urbana-Champaign, 2004.
[ bib ]
[85] Martin V. Butz. Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. In GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 1835-1842. ACM Press, 2005.
[ bib ]
[86] Martin V. Butz. Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design, volume 109 of Studies in Fuzziness and Soft Computing. Springer, 2006.
[ bib ]
[87] Martin V. Butz. Personal communication, 2007.
[ bib ]
[88] Martin V. Butz, Pier Luca Lanzi, Xavier Llorà, and Daniele Loiacono. An analysis of matching in learning classifier systems. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1349-1356. ACM, 2008.
[ bib ]
[89] Martin V. Butz, Pier Luca Lanzi, and Stewart W. Wilson. Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1457-1464. ACM Press, 2006.
[ bib ]
[90] Martin V. Butz and Martin Pelikan. Studying XCS/BOA learning in boolean functions: structure encoding and random boolean functions. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1449-1456. ACM, 2006.
[ bib ]
[91] Martin V. Butz, Martin Pelikan, Xavier Llorà, and David E. Goldberg. Automated global structure extraction for effective local building block processing in XCS. Evol. Comput., 14(3):345-380, 2006.
[ bib ]
[92] Martin V. Butz, Kumara Sastry, and David E. Goldberg. Tournament selection in XCS. In Proceedings of the Genetic and Evolutionary Computation Conference - GECCO2003, pages 1857-1869. LNCS 2724, Springer-Verlag, 2003.
[ bib ]
[93] Martin V. Butz, Kumara Sastry, and David E. Goldberg. Strong, stable, and reliable fitness pressure in XCS due to tournament selection. Genetic Programming and Evolvable Machines, 6(1):53-77, 2005.
[ bib ]
[94] M.V. Butz, P.L. Lanzi, and S.W. Wilson. Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. Evolutionary Computation, IEEE Transactions on, 12(3):355-376, 2008.
[ bib ]
[95] E. Cantu-Paz and C. Kamath. Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 7(1):54-68, Feb 2003.
[ bib ]
[96] Erick Cantu-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 2000.
[ bib ]
[97] Erick Cantú-Paz. On random numbers and the performance of genetic algorithms. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 311-318. Morgan Kaufmann Publishers, 2002.
[ bib ]
[98] J. Catlett. On changing continuous attributes into ordered discrete attributes. In Proceedings of the European working session on learning on Machine learning, pages 164-178. Springer-Verlag New York, Inc., 1991.
[ bib ]
[99] Michael J. Cavaretta and Kumar Chellapilla. Data mining using genetic programming: The implications of parsimony on generalization error. In Proceedings of the Congress on Evolutionary Computation, volume 2, pages 1330-1337. IEEE Press, 1999.
[ bib ]
[100] Jesus Cerquides and Ramon Lopez de Mantaras. Proposal and empirical comparison of a parallelizable distance-based discretization method. In III International Conference on Knowledge Discovery and Data Mining, pages 139-142. AAAI Press, 1997.
[ bib | .html ]
[101] C.-C. Chan, C. Batur, and A. Srinivasan. Determination of quantization intervals in rule based model for dynamic systems. In Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, pages 1719- 1723. IEEE Press, 1991.
[ bib ]
[102] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines. Department of Computer Science and Information Engineering, National Taiwan University, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[ bib ]
[103] Huiling Chen and Huan-Xiang Zhou. Prediction of solvent accessibility and sites of deleterious mutations from protein sequence. Nucleic Acids Res, 33:3193-3199, 2005.
[ bib ]
[104] Jianlin Cheng and Pierre Baldi. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinformatics, 8(113), 2007.
[ bib ]
[105] Marek Cieplak, Neal S. Holter, Amos Maritan, and Jayanth R. Banavar. Amino acid classes and the protein folding problem. Chem Phys, 114(3):1420-1423, 2001.
[ bib ]

Keywords: proteins; molecular biophysics; thermodynamic properties; matrix algebra; organic compounds
[106] P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Proc. Fifth European Working Session on Learning, pages 151-163. Springer-Verlag, 1991.
[ bib | .html ]
[107] Peter Clark and Tim Niblett. The cn2 induction algorithm. Machine Learning, 3:261-283, 1989.
[ bib | .html ]
[108] Melissa S. Cline, Kevin Karplus, Richard H. Lathrop, Temple F. Smith, Robert G. Rogers, and David Haussler. Information-theoretic dissection of pairwise contact potentials. Proteins, 49:7-14, 2002.
[ bib ]
[109] Carlos A. Coello. An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys, 32(2):109-143, 2000.
[ bib | .html ]
[110] William W. Cohen. Fast effective rule induction. In Armand Prieditis and Stuart Russell, editors, Proc. of the 12th International Conference on Machine Learning, pages 115-123, Tahoe City, CA, 1995. Morgan Kaufmann.
[ bib | .html ]
[111] A. L. Corcoran and S. Sen. Using real-valued genetic algorithms to evolve rule sets for classification. In Proceedings of the IEEE Conference on Evolutionary Computation, pages 120-124. IEEE Press, 1994.
[ bib | .html ]
[112] O. Cordón, M. del Jesus, and F. Herrera. Genetic learning of fuzzy rule-based classification systems co-operating with fuzzy reasoning methods. In International Journal of Intelligent Systems, Vol. 13 (10/11), pages 1025-1053, 1998.
[ bib | .html ]
[113] O. Cordon, F. Herrera, J. delaMontaña, A. M. Sánchez, and P. Villar. Aprendizaje evolutivo de un sistema basado en reglas difusas para la predicción de enfermedades cardiovasculares. In Proceedings of the ``Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)'', pages 183-190, 2002.
[ bib ]
[114] O. Cordon, F. Herrera, and P. Villar. Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base. IEEE Transactions on Fuzzy Systems, 9(4):667-674, Aug 2001.
[ bib ]
[115] Oscar Cordón, Francisco Herrera, Frank Hoffmann, and Luis Magdalena. Genetic Fuzzy Systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, 2001.
[ bib ]
[116] J.L. Cornette, K.B. Cease, H. Margalit, J.L. Spouge, J.A. Berzofsky, and C. DeLisi. Hydrophobicity scales and computational techniques for detecting amphipathic structures in proteins. J Mol Biol, 195(3):659-685, 1987.
[ bib ]
[117] Garrison W. Cottrell. Extracting features from faces using compression networks: Face, identity, emotion, and gender recognition using holons. In David S. Touretzky, Jeffrey L. Elman, Terrence J. Sejnowski, and Geoffrey E. Hinton, editors, Connectionist Models: Proceedings of the 1990 Summer School, pages 328-337. Morgan Kaufmann, 1990.
[ bib ]
[118] Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. John Wiley & sons, 1991.
[ bib ]

Keywords: information theory, statistics, probabilities
[119] James A. Cuff and Geoffrey J. Barton. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins, 34:508-519, 1999.
[ bib ]
[120] Dipankar Dasgupta and Fabio A. Gonzalez. Evolving complex fuzzy classifier rules using a linear tree genetic representation. In Proceedings of the Third Genetic and Evolutionary Computation Conference, pages 299-305. Morgan Kaufmann, 2001.
[ bib ]
[121] Lawrence Davis. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, 1991.
[ bib ]
[122] Kenneth A. De Jong. An Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, 1975.
[ bib ]
[123] Kenneth A. De Jong. Learning with genetic algorithms: An overview. Mach. Learn., 3(2-3):121-138, 1988.
[ bib ]
[124] Kenneth A. De Jong and William M. Spears. Learning concept classification rules using genetic algorithms. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 651-656. Morgan Kaufmann, 1991.
[ bib ]
[125] Kenneth A. De Jong, William M. Spears, and Diana F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13(2/3):161-188, 1993.
[ bib | .html ]
[126] Luis de la Ossa, Kumara Sastry, and Fernando G. Lobo. chi-ary extended compact genetic algorithm in c++. Technical Report 2006013, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 2006.
[ bib ]
[127] R. López De Mántaras. A distance-based attribute selection measure for decision tree induction. Machine Learning, 6(1):81-92, 1991.
[ bib ]
[128] Janez Demsar. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 7:1-30, 2006.
[ bib ]
[129] Ken A. Dill. Theory for the folding and stability of globular proteins. Biochemistry, 24(6):1501-1509, March 1985.
[ bib | http ]

Keywords: graph_drawing, msc
[130] F. Divina, M. Keijzer, and E. Marchiori. A method for handling numerical attributes in GA-based inductive concept learners. In GECCO 2003: Proceedings of the Genetic and Evolutionary Computation Conference, pages 898-908. Springer-Verlag, 12-16 July 2003.
[ bib ]

Keywords: genetic algorithms
[131] Federico Divina. Evolutionary concept learning in first order logic: An overview. AI Commun., 19(1):13-33, 2006.
[ bib ]
[132] Federico Divina and Elena Marchiori. Handling continuous attributes in an evolutionary inductive learner. IEEE Transactions on Evolutionary Computation, 9(1):31-43, February 2005.
[ bib ]
[133] Pedro Domingos. The rise system: Conquering without separating. In Proceedings of the Sixth IEEE International Conference on Tools with Artificial Intelligence, pages 704-707. IEEE Press, 1994.
[ bib | .html ]
[134] Pedro Domingos. Rule induction and instance-based learning: A unified approach. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1226-1232. Morgan Kaufmann, 1995.
[ bib | .html ]
[135] Pedro Domingos. Unifying instance-based and rule-based induction. Machine Learning, 24(2):141-168, 1996.
[ bib | .html ]
[136] Ofer Dor and Yaoqi Zhou. Achieving 80 structure prediction by large-scale training. Proteins, 66(4):838-845, 2007.
[ bib ]
[137] Jan Drugowitsch and Alwyn M. Barry. Mixing independent classifiers. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1596-1603. ACM Press, 2007.
[ bib ]
[138] Jan Drugowitsch and Alwyn M. Barry. A principled foundation for lcs. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2675-2680. ACM Press, 2007.
[ bib ]
[139] Aniko Ekart and S. Z. Nemeth. Selection based on the pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, 2(1):61-73, 2001.
[ bib ]
[140] Tapio Elomaa and Juho Rousu. Fast minimum training error discretization. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 131-138. Morgan Kaufmann Publishers Inc., 2002.
[ bib ]
[141] G. Escuela, G. Ochoa, and N. Krasnogor. Evolving l-systems to capture protein structure native conformations. In Maarten Keijzer, Andrea Tettamanzi, Pierre Collet, Jano van Hemert, and Marco Tomassini, editors, 8th European Conference on Genetic Programming, volume 3447 of Springer Lecture Notes in Computer Science, pages 73-83, Lausanne, Switzerland, March, April 2005. EuroGP, Springer. ISBN 978-3-540-25436-2. Nominated for Best Paper Award.
[ bib | .pdf ]
[142] G. Escuela, G. Ochoa, and N. Krasnogor. Evolving l-systems to capture protein structure native conformations. In Proceedings of the 8th European Conference on Genetic Programming (EuroGP 2005), Lecture Notes in Computer Sciences 3447, pp 73-84. Springer-Verlag, 2005.
[ bib ]
[143] David Baker et al. Rosetta@home web page. http://boinc.bakerlab.org/rosetta/.
[ bib ]
[144] David Baker et al. Rosetta@home web page. http://boinc.bakerlab.org/rosetta/.
[ bib ]
[145] C. Etchebest, C. Benros, A. Bornot, A. C. Camproux, and A. de Brevern. A reduced amino acid alphabet for understanding and designing protein adaptation to mutation. European Biophysics Journal, 36(8):1059-1069, 2007.
[ bib ]
[146] P. Fariselli, O. Olmea, A. Valencia, and R. Casadio. Progress in predicting inter-residue contacts of proteins with neural networks and correlated mutations. Proteins, suppl 5:157-162, 2001.
[ bib ]
[147] Usama Fayyad, Gregory Piatetsky-shapiro, and Padhraic Smyth. From data mining to knowledge discovery in databases. AI Magazine, 17:37-54, 1996.
[ bib ]
[148] Usama M. Fayyad and Keki B. Irani. Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1022-1029. Morgan Kaufmann, 1993.
[ bib ]
[149] L. J. Fogel. On the Organization of Intellect. PhD thesis, University of California, Los Angeles, 1964.
[ bib ]
[150] R.S. Forsyth, David D. Clarke, and Richard L. Wright. Overfitting revisited: An information-theoretic approach to simplifying discrimination trees. Journal of Experimental and Theoretical Artificial Intelligence, 6(3):289-302, 1994.
[ bib ]
[151] Eibe Frank and Mark Hall. A simple approach to ordinal classification. In Proc 12th European Conference on Machine Learning, pages 145-156. Springer, 2001.
[ bib ]
[152] Eibe Frank and Ian H. Witten. Generating accurate rule sets without global optimization. In Proc. 15th International Conf. on Machine Learning, pages 144-151. Morgan Kaufmann, San Francisco, CA, 1998.
[ bib | .html ]
[153] Alex A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, 2002.
[ bib ]
[154] Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In International Conference on Machine Learning, pages 148-156, 1996.
[ bib | .html ]
[155] Johannes Fürnkranz. Integrative windowing. Journal of Artificial Intelligence Research, 8:129-164, 1998.
[ bib | .html ]
[156] Johannes Fürnkranz. Separate-and-conquer rule learning. Artificial Intelligence Review, 13(1):3-54, February 1999.
[ bib | .html ]
[157] Johannes Furnkranz and Gerhard Widmer. Incremental reduced error pruning. In International Conference on Machine Learning, pages 70-77, 1994.
[ bib | .html ]
[158] Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy. Mining data streams: a review. SIGMOD Rec., 34(2):18-26, 2005.
[ bib ]
[159] Qiong Gao, Ming Li, and Paul Vitányi. Applying mdl to learn best model granularity. Artificial Intelligence, 121(1-2):1-29, 2000.
[ bib ]
[160] Aarti Garg, Harpreet Kaur, and G.P.S. Raghava. Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins, 61:318-324, 2005.
[ bib ]
[161] Chris Gathercole and Peter Ross. Dynamic training subset selection for supervised learning in genetic programming. In Yuval Davidor, Hans-Paul Schwefel, and Reinhard Männer, editors, Parallel Problem Solving from Nature III, volume 866, pages 312-321, Jerusalem, 9-14 1994. Springer-Verlag.
[ bib | .html ]
[162] G. Gianese, F. Bossa, and S. Pascarella. Improvement in prediction of solvent accessibility by probability profiles. Protein Eng, 16:987-992, 2003.
[ bib ]
[163] R. Giráldez, J. S. Aguilar-Ruiz, and J. C. Riquelme. Discretización supervisada no paramétrica orientada a la obtención de reglas de decisión. In Proceedings of the CAEPIA2001, pages 53-62, 2001.
[ bib ]
[164] R. Giráldez, J. S. Aguilar-Ruiz, J. C. Riquelme, J.F. Ferrer, and D.S. Rodríguez. Discretization oriented to decision rule generation. In Proceedings of the International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES'02, pages 275-279. IOS Press, 2002.
[ bib ]
[165] R. Giráldez, J.S. Aguilar-Ruiz, and J.C. Riquelme. Natural coding: A more efficient representation for evolutionary learning. In GECCO 2003: Proceedings of the Genetic and Evolutionary Computation Conference, pages 979-990. Springer-Verlag, 12-16 July 2003.
[ bib ]

Keywords: genetic algorithms
[166] Raúl Giráldez and Jesús S. Aguilar-Ruiz. Feature influence for evolutionary learning. In GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, volume 2, pages 1139-1145. ACM Press, 2005.
[ bib ]
[167] Raúl Giráldez, Jesús S. Aguilar-Ruiz, and José Cristóbal Riquelme Santos. Knowledge-based fast evaluation for evolutionary learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 35(2):254-261, 2005.
[ bib ]
[168] Christophe Giraud-Carrier. A note on the utility of incremental learning. AI Commun., 13(4):215-223, 2000.
[ bib ]
[169] S.A. Glantz and B.K. Slinker. Primer of Applied Regression and Analysis of Variance. McGraw-Hill, 2000.
[ bib ]
[170] D. E. Goldberg. Sizing populations for serial and parallel genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms (ICGA89), pages 70-79. Morgan Kaufmann, 1989.
[ bib ]
[171] David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc., 1989.
[ bib ]
[172] David E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, 2002.
[ bib ]
[173] David E. Goldberg and Kalyanmoy Deb. A comparative analysis of selection schemes used in genetic algorithms. In Foundations of Genetic Algorithms, pages 69-93. Morgan Kaufmann, 1991.
[ bib ]
[174] David E. Goldberg, Kumara Sastry, and Thomas Latoza. On the supply of building blocks. In Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 336-342, San Francisco, California, USA, 7-11 2001. Morgan Kaufmann.
[ bib | .html ]
[175] Elisabet Golobardes, Xavier Llorà, Josep Maria Garrell, David Vernet, and Jaume Bacardit. Genetic classifier system as a heuristic weighting method for a case-based classifier system. Butlletí de l'Associació Catalana d'Intel.ligència Artificial, 22:132-141, 2000.
[ bib ]
[176] C.H. Goulden. Methods of Statistical Analysis. John Wiley & Sons, New York, 1956.
[ bib ]
[177] John J. Grefenstette. The user's guide to samuel-97: An evolutionary learning system.
[ bib | .html ]
[178] John J. Grefenstette. Lamarckian learning in multi-agent environments. In Rick Belew and Lashon Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 303-310, San Mateo, CA, 1991. Morgan Kaufman.
[ bib | .html ]
[179] M Gribskov, A D McLachlan, and D Eisenberg. Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci U S A, 84(13):4355-4358, 1987.
[ bib ]
[180] Isabelle Guyon and Andre Elisseeff. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157-1182, 2003.
[ bib | http ]
[181] N. Hamilton, K. Burrage, M.A. Ragan, and T. Huber. Protein contact prediction using patterns of correlation. Proteins, 56:679-684, 2004.
[ bib ]
[182] G. Harik. Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty using Genetic Algorithms. PhD thesis, University of Michigan, Ann Arbor, 1995. Also available as IlliGAL Report 97005.
[ bib ]
[183] G. Harik. Linkage learning via probabilistic modeling in the ecga. Technical Report 99010, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 1999.
[ bib ]
[184] G. Harik, E. Cantu-Paz, D. E. Goldberg, and B. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 7-12. IEEE Press, 1997.
[ bib | .html ]
[185] G. R. Harik, F. G. Lobo, and D. E. Goldberg. The compact genetic algorithm. IEEE-EC, 3(4):287, November 1999.
[ bib | .html ]
[186] Georges R. Harik. Finding multimodal solutions using restricted tournament selection. In Larry Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 24-31, San Francisco, CA, 1995. Morgan Kaufmann.
[ bib | .html ]
[187] William Hart and Sorin Istrail. Robust proofs of NP-hardness for protein folding: General lattices and energy potentials. Journal of Computational Biology, pages 1-20, Spring 1997.
[ bib ]
[188] William E. Hart and Sorin Istrail. Crystallographical universal approximability: A complexity theory of protein folding algorithms on crystal lattices. Technical Report SAND95-1294, Sandia National Labs, Albuquerque, NM, 1995.
[ bib ]
[189] T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer, 2001.
[ bib ]
[190] Hai H.Dam, Chris Lokan, and Hussein A. Abbas. Evolutionary online data mining: An investigation in a dynamic environment. In Evolutionary Computation in Dynamic and Uncertain Environments, pages 153-178. Springer-Verlag, 2007.
[ bib ]
[191] S Henikoff and J G Henikoff. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A, 89(22):10915-10919, 1992.
[ bib ]
[192] D. A. Hinds and M. Levitt. A lattice model for protein-structure prediction at low resolution. Proc. National Academy Sciences U.S.A., 89:2536-2540, 1992.
[ bib ]
[193] K. M. Ho and P. D. Scott. Zeta: A global method for discretization of continuous variables. In III International Conference on Knowledge Discovery and Data Mining, pages 191-194. AAAI Press, 1997.
[ bib | .html ]
[194] J. H. Holland and J. S. Reitman. Cognitive systems based on adaptive algorithms. In D. Hayes-Roth and F. Waterman, editors, Pattern-directed Inference Systems, pages 313-329. Academic Press, New York, 1978.
[ bib ]
[195] John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.
[ bib ]
[196] John H. Holland. Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems. In Machine learning, an artificial intelligence approach. Volume II, pages 593-623. 1986.
[ bib ]
[197] Myles Hollander and Douglas A. Wolfe. Nonparametric statistical inference. John Wiley & Sons, New York, 1973.
[ bib ]
[198] John H. Holmes, Dennis R. Durbin, and Flaura K. Winston. The learning classifier system: an evolutionary computation approach to knowledge discovery in epidemiologic surveillance. Artificial Intelligence In Medicine, 19(1):53-74, 2000.
[ bib ]
[199] R. C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-91, 1993.
[ bib ]
[200] Francesc Xavier Llorà i Fàbrega. Classificaci´o automàtica emprant algorismes genètics sota un enfocament de Pittsburgh. Projecte final de carrera s0302, Enginyeria La Salle, Universitat Ramon Llull, 1998.
[ bib ]
[201] Jaume Bacardit i Peñarroya. Creaci´o d'un Entorn Educatiu de Computaci´o Evolutiva sobre Visual C++. Treball final de carrera 2343, Enginyeria La Salle, Universitat Ramon Llull, 1998.
[ bib ]
[202] Jaume Bacardit i Peñarroya. Creaci´o d'un assistent digital per a la predicci´o del fracàs acadèmic basat en computaci´o evolutiva. Proyecto final de carrera s0491, Enginyeria La Salle, Universitat Ramon Llull, 2000.
[ bib ]
[203] Hitoshi Iba, Hugo de Garis, and Taisuke Sato. Genetic programming using a minimum description length principle. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, pages 265-284. MIT Press, 1994.
[ bib | .html ]
[204] Yasuhiro Ikenaka, Hirokazu Nanba, Kazuyoshi Yajima, Yukio Yamada, Masayuki Takano, and Satomi Takahashi. Increase in thermostability of N-carbamyl-d-amino acid amidohydrolase on amino acid substitutions. Bioscience, Biotechnology, and Biochemistry, 62(9):1668-1671, 1998.
[ bib ]
[205] Yasuhiro Ikenaka, Hirokazu Nanba, Yukio Yamada, Kazuyoshi Yajima, Masayuki Takano, and Satomi Takahashi. Screening, characterization, and cloning of the gene for N-carbamyl-d-amino acid amidohydrolase from thermotolerant soil bacteria. Bioscience, Biotechnology, and Biochemistry, 62(5):882-886, 1998.
[ bib ]
[206] C. Janikow. Indictive Learning of Decision Rules in Attribute-Based Examples: a Knowledge-Intensive Genetic Algorithm Approach. PhD thesis, University of North Carolina, 1991.
[ bib ]
[207] Cezary Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Mach. Learn., 13(2-3):189-228, 1993.
[ bib ]
[208] Fernando Jiménez, Antonio F. Gómez-Skarmeta, Hans Roubos, and Robert Babuska. Accurate, transparent and compact fuzzy models for function approximation and dynamic modeling through multi-objective evolutionary optimization. In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization, pages 653-667. Springer-Verlag, 2001.
[ bib ]
[209] Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput., 9(1):3-12, 2005.
[ bib ]
[210] Thorsten Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Kluwer Academic Publishers, 2002.
[ bib ]
[211] George H. John and Pat Langley. Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338-345. Morgan Kaufmann Publishers, San Mateo, 1995.
[ bib | .html ]
[212] George H. John and Pat Langley. Static versus dynamic sampling for data mining. In Evangelos Simoudis, Jiawei Han, and Usama M. Fayyad, editors, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 367-370. AAAI Press, 2-4  1996.
[ bib | .html ]
[213] D.T. Jones. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol, 292:195-202, 1999.
[ bib ]
[214] Terry Jones. Crossover, macromutation, and population-based search. In Larry Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 73-80, San Francisco, CA, 1995. Morgan Kaufmann.
[ bib | .html ]
[215] WOLFGANG KABSCH and CHRISTIAN SANDER. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22:2577-2637, 1983.
[ bib ]
[216] J. Kalbfleish. Probability and Statistical Inference, volume II. Springer-Verlag, 1979.
[ bib ]
[217] S Kamtekar, JM Schiffer, H Xiong, JM Babik, and MH Hecht. Protein design by binary patterning of polar and nonpolar amino acids. Science, 262(5140):1680-1685, 1993.
[ bib ]
[218] K. A. Kaufman and R. S. Michalski. Learning from inconsistent and noisy data: The AQ18 approach. 1609:411-419, 1999.
[ bib ]
[219] A. Kavcic and M. Srinivasan. The minimum description length principle for modeling recording channels. IEEE Journal on Selected Areas in Communications, 19(4):719-729, 2001.
[ bib ]
[220] Michael J. Kearns and Umesh V. Vazirani. An introduction to computational learning theory. MIT Press, Cambridge, MA, USA, 1994.
[ bib ]
[221] R. Kerber. Chimerge: Discretization of numeric attributes. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 123-128, San Jose, CA, 1992. AAAI Press.
[ bib ]
[222] Faten Kharbat, Larry Bull, and Mohammed Odeh. Mining breast cancer data with XCS. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 2066-2073. ACM, 2007.
[ bib ]
[223] Akira R. Kinjo, Katsuhisa Horimoto, and Ken Nishikawa. Predicting absolute contact numbers of native protein structure from amino acid sequence. Proteins, 58:158-165, 2005.
[ bib ]
[224] Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1137-1145. Morgan Kaufmann, 1995.
[ bib | .html ]
[225] J. R. Koza. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.
[ bib ]
[226] John Koza. The 2007 'humies' awards for human-competitive results produced by genetic and evolutionary computation, 2007. http://www.genetic-programming.org/hc2007/cfe2007.html.
[ bib ]
[227] Alexander V. Kozlov and Daphne Koller. Nonuniform dynamic discretization in hybrid networks. In Proceedings of the Thirteenth Annual Conference on Uncertainty in AI (UAI), pages 314-325. Morgan Kaufmann, 1997.
[ bib | .html ]
[228] S. Kramer. Structural regression trees. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pages 812-819, Cambridge/Menlo Park, 1996. AAAI Press/MIT Press.
[ bib | .html ]
[229] Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer, and Michael de Groeve. Prediction of ordinal classes using regression trees. Fundam. Inform., 47(1-2):1-13, 2001.
[ bib | .ps | .pdf ]

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using class ification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it in to a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to ver ify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classificat ion accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.
[230] N. Krasnogor, B.P. Blackburne, E.K. Burke, and J.D. Hirst. Multimeme algorithms for protein structure prediction. In Proceedings of the Parallel Problem Solving from Nature VII. Lecture Notes in Computer Science, volume 2439, pages 769-778, 2002.
[ bib ]
[231] N. Krasnogor, E. de la Cananl, D.A. Pelta, D.H Marcos, and W.A. Risi. Encoding and crossover mismatch in a molecular design problem. In P. Bentley, editor, AID98: Proceedings of the Workshop on Artificial Intelligence in Design 1998, 1998.
[ bib ]
[232] N. Krasnogor, W.E. Hart, J. Smith, and D. Pelta. Protein structure prediction with evolutionary algorithms. In International Genetic and Evolutionary Computation Conference (GECCO99), pages 1569-1601. Morgan Kaufmann, 1999.
[ bib | .pdf ]
[233] N. Krasnogor, W.E. Hart, J. Smith, and D.A. Pelta. Protein structure prediction with evolutionary algorithms. In W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakaiela, and R.E. Smith, editors, GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 1999.
[ bib ]
[234] N. Krasnogor and D. A. Pelta. Fuzzy Sets based Heuristics for Optimization, volume 126 of Studies in Fuzziness and Soft Computing, chapter Fuzzy Memes in Multimeme Algorithms: a Fuzzy-Evolutionary Hybrid, pages 49-66. Springer-Verlag, 2002.
[ bib | .pdf ]
[235] N. Krasnogor and D.A. Pelta. Fuzzy memes in multimeme algorithms: a fuzzy-evolutionary hybrid. In J.L. Verdegay, editor, Fuzzy Sets based Heuristics for Optimization. Springer, 2002.
[ bib ]
[236] N. Krasnogor, D.A. Pelta, D.H. Marcos, and W.A. Risi. Protein structure prediction as a complex adaptive system. In Proceedings of Frontiers in Evolutionary Algorithms 1998, 1998.
[ bib ]
[237] N. Krasnogor and J.E. Smith. A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation, 9(5):474- 488, 2005.
[ bib | .pdf ]
[238] S. Kullback and R. A. Leibler. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79-86, 1951.
[ bib ]

Keywords: gain, information
[239] Wojciech Kwedlo and Marek Kretowski. An evolutionary algorithm for cost-sensitive decision rule learning. In Proceedings of the 12th European Conference on Machine Learning, pages 288-299. Springer-Verlag, 2001.
[ bib ]
[240] A. J. Bagnall L. Bull, M. Studley and I. Whittley. On the use of rule sharing in learning classifier system ensembles. In Proceedings of the 2005 Congress on Evolutionary Computation, 2005.
[ bib ]
[241] K. J. Lang, G. E. Hinton, and A. Waibel. A time-delay neural network architecture for isolated word recognition. Neural Networks, 3(1):23-43, 1990.
[ bib ]
[242] W. B. Langdon. Fitness causes bloat in variable size representations. Technical Report CSRP-97-14, University of Birmingham, School of Computer Science, 1997. Position paper at the Workshop on Evolutionary Computation with Variable Size Representation at ICGA-97.
[ bib | .ps.gz ]

We argue based upon the numbers of representations of given length, that increase in representation length is inherent in using a fixed evaluation function with a discrete but variable length representation. Two examples of this are analysed, including the use of Price's Theorem. Both examples confirm the tendency for solutions to grow in size is caused by fitness based selection.

Keywords: genetic algorithms, genetic programming, bloat, variable size representation
[243] Pat Langley. Elements of machine learning. Morgan Kaufmann Publishers Inc., 1995.
[ bib ]
[244] Pat Langley, Wayne Iba, and Kevin Thompson. An analysis of bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 223-228. AAAI Press, 1992.
[ bib | .html ]
[245] Pier Luca Lanzi. Learning classifier systems: then and now. Evolutionary Intelligence, 1(1):63,82, 2008.
[ bib | http ]
[246] Pier Luca Lanzi and Daniele Loiacono. Classifier systems that compute action mappings. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1822-1829. ACM Press, 2007.
[ bib ]
[247] Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wilson, and David E. Goldberg. Extending XCSF beyond linear approximation. In GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 1827-1834. ACM, 2005.
[ bib ]
[248] Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wilson, and David E. Goldberg. Prediction update algorithms for XCSF: RLS, kalman filter, and gain adaptation. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1505-1512. ACM Press, 2006.
[ bib ]
[249] Pier Luca Lanzi and Alessandro Perrucci. Extending the representation of classifier conditions part ii: From messy coding to s-expressions. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 345-352, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann.
[ bib | www ]

Keywords: genetic algorithms and classifier systems
[250] Pier Luca Lanzi, Stefano Rocca, and Stefania Solari. An approach to analyze the evolution of symbolic conditions in learning classifier systems. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2795-2800. ACM Press, 2007.
[ bib ]
[251] P. Larranaga and J.A. Lozano, editors. Estimation of Distribution Algorithms, A New Tool for Evolutionnary Computation. Genetic Algorithms and Evolutionnary Computation. Kluwer Academic Publishers, 2002.
[ bib ]
[252] E.E. Lattman. Sixth meeting on the critical assessment of techniques for protein structure prediction. Proteins, 61(S7):1-2, 2005.
[ bib ]
[253] Nada Lavrac and Saso Dzeroski. Inductive Logic Programming: Techniques and Applications. Routledge, 1993.
[ bib ]
[254] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, Winter 1989.
[ bib ]
[255] M. Lehtokangas, J. Saarinen, P. Huuhtanen, and K. Kaski. Predictive minimum description length criterion for time series modeling with neural networks. Neural Computation, 8(3):583-593, 1996.
[ bib ]
[256] Arthur M. Lesk. Introduction to Protein Structure. Oxford University Press, 2001.
[ bib ]
[257] T. Li, K. Fan, J. Wang, and W. Wang. Reduction of protein sequence complexity by residue grouping. Protein Eng, 16(5):323-330, 2003.
[ bib ]
[258] Tanping Li, Ke Fan, Jun Wang, and Wei Wang. Reduction of protein sequence complexity by residue grouping. Protein Eng., 16(5):323-330, 2003.
[ bib ]
[259] Tanping Li, Ke Fan, Jun Wang, and Wei Wang. Reduction of protein sequence complexity by residue grouping. Protein Engineering, 16:323-330(8), 2003.
[ bib ]
[260] Roderick J A Little and Donald B Rubin. Statistical analysis with missing data. John Wiley & Sons, Inc., 1986.
[ bib ]
[261] H. Liu, F. Hussain, C. Lim Tam, and M. Dash. Discretization: An enabling technique. Data Mining and Knowledge Discovery, 6(4):393-423, 2002.
[ bib ]
[262] H. Liu and R. Setiono. Chi2: Feature selection and discretization of numeric attributes. In Proceedings of Seventh IEEE International Conference on Tools with Artificial Intelligence, pages 388-391. IEEE Computer Society, 1995.
[ bib | .html ]
[263] Huan Liu, Farhan Hussain, Chew Lim Tan, and Manoranjan Dash. Discretization: An enabling technique. Data Mining and Knowledge Discovery, 6(4):393-423, 2002.
[ bib ]
[264] Xin Liu, Li-mei Zhang, Shan Guan, and Wei-Mou Zheng. Distances and classification of amino acids for different protein secondary structures. Phys. Rev. E, 67(5):051927, May 2003.
[ bib ]
[265] C. D. Livingstone and Geoffrey J. Barton. Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation. Computer Applications in the Biosciences, 9(6):745-756, 1993.
[ bib ]
[266] X. Llorà, A. Priya, and R. Bhargava. Observer-invariant histopathology using genetics-based machine learning. Technical Report 2006027, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 2006.
[ bib ]
[267] X. Llorà, A. Priya, and R. Bhargava. Observer-invariant histopathology using genetics-based machine learning. Natural Computing, page in press, 2008.
[ bib ]
[268] X Llora, K Sastry, and D.E. Goldberg. The compact classifier system: Scalability analysis and first results. In Proceedings of the Congress on Evolutionary Computation 2005, volume 1, pages 596-603. IEEE Press, 2005.
[ bib ]
[269] X. Llorá and S.W. Wilson. Mixed decision trees: Minimizing knowledge representation bias in lcs. In GECCO 2004: Proceedings of the Genetic and Evolutionary Computation Conference, pages 797-809. Springer-Verlag, LNCS 3103, 2004.
[ bib ]
[270] Xavier Llorà. Personal communication, 2008.
[ bib ]
[271] Xavier Llorà, Jaume Bacardit, Ester Bernadó, and Ivan Traus. Where to go once you have evolved a bunch of promising hypotheses? In Advances at the frontier of Learning Classifier Systems (Volume I). (in press), LNAI, Springer-Verlag, 2006.
[ bib ]
[272] Xavier Llorà and Josep M. Garrell. Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In Proceedings of the Third Genetic and Evolutionary Computation Conference, pages 461-468. Morgan Kaufmann, 2001.
[ bib ]
[273] Xavier Llorà and Josep Maria Garrell. Inducing partially-defined instances with evolutionary algorithms. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 337-344. Morgan Kaufmann, 2001.
[ bib ]
[274] Xavier Llorà, David E. Goldberg, Ivan Traus, and Ester Bernadó. Accuracy, Parsimony, and Generality in Evolutionary Learning System a Multiobjective Selection. In Advances in Learning Classifier Systems: proceedings of the 5th International Workshop on Learning Classifier Systems. (in press), LNAI, Springer-Verlag, 2002.
[ bib ]
[275] Xavier Llorà, David E. Goldberg, Ivan Traus, and Ester Bernadó. Accuracy, parsimony, and generality in evolutionary learning systems via multiobjective selection. Technical Report 2002016, Illinois Genetic Algorithms Lab, University of Illinois at Urbana-Champaign, 2002.
[ bib ]
[276] Xavier Llorà, Rohith Reddy, Brian Matesic, and Rohit Bhargava. Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 2098-2105. ACM Press, 2007.
[ bib ]
[277] Xavier Llorà and Kumara Sastry. Fast rule matching for learning classifier systems via vector instructions. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1513-1520. ACM Press, 2006.
[ bib ]
[278] Xavier Llorà, Kumara Sastry, David E. Goldberg, and Luis delaOssa. The x-ary extended compact classifier system: Linkage learning in pittsburgh lcs. In Proceedings of the 9th International Workshop on Learning Classifier Systems - IWLCS2006. (in press), LNAI, Springer-Verlag, 2006.
[ bib ]
[279] Xavier Llorà, Kumara Sastry, Cláudio F. Lima, Fernando G. Lobo, and David E. Goldberg. Linkage learning, rule representation, and the x-ary extended compact classifier system. In Learning Classifier Systems, Revised Selected Papers of IWLCS 2006-2007, pages 189-205. LNAI 4998, Springer-Verlag, 2008.
[ bib ]
[280] Xavier Llorà, Kumara Sastry, Tian-Li Yu, and David E. Goldberg. Do not match, inherit: fitness surrogates for genetics-based machine learning techniques. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1798-1805. ACM, 2007.
[ bib ]
[281] Daniele Loiacono, Andrea Marelli, and Pier Luca Lanzi. Support vector regression for classifier prediction. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1806-1813. ACM Press, 2007.
[ bib ]
[282] M. Lozano, F. Herrera, N. Krasnogor, and D. Molina. Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation, 12(3):273-302, 2004.
[ bib | .pdf ]
[283] P. Luca Lanzi and D. Loiacono. XCSF with neural prediction. Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 2270-2276, 0-0 0.
[ bib ]

Keywords: genetic algorithms, multilayer perceptrons, pattern classificationXCSF, classifier, feedforward multilayer neural network, genetic algorithm, payoff surface
[284] Sean Luke and Liviu Panait. Lexicographic parsimony pressure. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 829-836. Morgan Kaufmann, 2002.
[ bib ]
[285] RM. MacCallum. Striped sheets and protein contact prediction. Bioinformatics, 20:I224-I231, 2004.
[ bib ]
[286] Marcus A. Maloof and Ryszard S. Michalski. Selecting examples for partial memory learning. Machine Learning, 41(1):27-52, 2000.
[ bib | .html ]
[287] Y. Mandel-Gutfreund and L.M. Gregoret. On the significance of alternating patterns of polar and non-polar residues in beta-strands. Journal of Molecular Biology, 323(9):453-461, 2002.
[ bib ]
[288] James A. R. Marshall, Gavin Brown, and Tim Kovacs. Bayesian estimation of rule accuracy in ucs. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2831-2834. ACM Press, 2007.
[ bib ]
[289] J. Martí, X. Cufí, J. Regincós, and et al. Shape-based feature selection for microcalcification evaluation. In Proceedings of the SPIE Medical Imaging Conference on Image Processing, pages 1215-1224. SPIE Press, 1998.
[ bib ]
[290] E. Martínez Marroquín, C. Vos, and et al. Morphological analysis of mammary biopsy images. In Proceedings of the IEEE International Conference on Image Processing, pages 943-947. IEEE Press, 1996.
[ bib ]
[291] Makoto Matsumoto and Takuji Nishimura. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS), 8(1):3-30, 1998.
[ bib ]
[292] W.S. McCulloch and W. Pitts. A logical calculus of ideas immanent in neural activity. Bulletin of Mathematical Biophysics, 5:115-133, 1943.
[ bib ]
[293] Jens Meiler, Michael Müand Anita Zeidler, and Felix Schmähke. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. J Mol Model, 7:360-369, 2001.
[ bib ]
[294] F. Melo and M.A. Marti-Renom. Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets. Proteins, 63:986-995, 2006.
[ bib ]
[295] Mark M. Meyesenburg, Daniel Hoelting, Duane McElvain, and James A. Foster. How random generator quality impacts GA performance. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 480-487. Morgan Kaufmann Publishers, 2002.
[ bib ]
[296] Zbigniew Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 1996.
[ bib ]
[297] R. Michalski. On the quasi-minimal solution of the general covering problem. In Proceedings of the 5th International Symposium on Information Processing (FCIP-69), volume A3, pages 125-128, 1969.
[ bib ]
[298] R. S. Michalski, I. Mozetic, and J. Hong. The multi-purpose incremental learning system aq15 and it testing application to three medical domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, pages 1041-1045. AAAI Press, 1986.
[ bib ]
[299] Ryszard S. Michalski, Igor Mozetic, Jiarong Hong, and Nada Lavrac. The AQ15 inductive learning system: an overview and experiments. Technical Report UIUCDCS-R-86-1260, University of Illinois, July 1986.
[ bib ]
[300] R. G. Miller. Simultaneous Statistical Inference. Springer Verlag, New York, 1981.
[ bib ]
[301] S. Miller, J. Janin, A.M. Lesk, and C. Chothia. Interior and surface of monomeric proteins. J Mol Biol, 196(3):641-656, 1987.
[ bib ]
[302] J. Mintseris and Z. Weng. Optimizing protein representations with information theory. Genome Informatics, 15(1):160-169, 2004.
[ bib ]
[303] K. M. Misura, D. Chivian, C. A. Rohl, D. E. Kim, and D. Baker. Physically realistic homology models built with rosetta can be more accurate than their templates. Proc Natl Acad Sci U S A, 103(14):5361-5366, 2006.
[ bib ]
[304] Tom M. Mitchell. Machine Learning. McGraw-Hill, 1997.
[ bib ]
[305] Sanzo Miyazawa and Robert L. Jernigan. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol, 256:623-644, 1996.
[ bib ]
[306] P. Moscato. On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA, 1989.
[ bib ]
[307] John Moult, Krzysztof Fidelis, Burkhard Rost, Tim Hubbard, and Anna Tramontano. Critical assessment of methods of protein structure prediction (casp) - round 6. Proteins: Structure, Function, and Bioinformatics, 61(S7):3-7, 2005.
[ bib ]
[308] Stephen Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:629-679, 1994.
[ bib | .html ]
[309] Lynne Reed Murphy, Anders Wallqvist, and Ronald M. Levy. Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Engineering, 13:149-152(4), 2000.
[ bib ]
[310] Lynne Reed Murphy, Anders Wallqvist, and Ronald M. Levy. Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Eng., 13(3):149-152, 2000.
[ bib | http ]
[311] Japkowicz N. and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5):429-450, 2002.
[ bib ]
[312] T. Niblett. Constructing decision trees in noisy domains. In I. Bratko and N. Lavrac, editors, Progress in Machine Learning, pages 67-78, Wilmslow, 1987. Sigma Press.
[ bib ]
[313] Nils J. Nilsson. Artificial intelligence: a new synthesis. Morgan Kaufmann Publishers Inc., 1998.
[ bib ]
[314] T. Noguchi, H. Matsuda, and Y. Akiyama. Pdb-reprdb: a database of representative protein chains from the protein data bank (pdb). Nucleic Acids Res, 29:219-220, 2001.
[ bib ]
[315] Peter Nordin and Wolfgang Banzhaf. Complexity compression and evolution. In L. Eshelman, editor, Genetic Algorithms: Proceedings of the Sixth International Conference, pages 310-317, Pittsburgh, PA, USA, 15-19 1995. Morgan Kaufmann.
[ bib | .html ]
[316] C. K. Oei, D. E. Goldberg, and S.-J. Chang. Tournament selection, niching, and the preservation of diversity. IlliGAL Report No. 91011, University of Illinois at Urbana-Champaign, Urbana, IL, 1991.
[ bib ]
[317] Toby O'Hara and Larry Bull. Backpropagation in accuracy-based neural learning classifier systems. In Advances at the frontier of Learning Classifier Systems, pages 25-39. Springer-Verlag, 2007.
[ bib ]
[318] Albert Orriols-Puig. New Challenges in Learning Classifier Systems: Mining Rarities and Evolving Fuzzy Models. PhD thesis, Ramon Llull University, Barcelona, Spain, 2008.
[ bib ]
[319] Albert Orriols-Puig and Ester Bernadó-Mansilla. A further look at ucs classifier system. In Proceedings of the 9th International Workshop on Learning Classifier Systems - IWLCS2006. Springer - to appear, 2006.
[ bib ]
[320] Albert Orriols-Puig, Ester Bernadó-Mansilla, Kumara Sastry, and David E. Goldberg. Substructrual surrogates for learning decomposable classification problems: implementation and first results. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2875-2882. ACM, 2007.
[ bib ]
[321] Vilfredo Pareto. Cours D'Economie Politique, volume I and II. F. Rouge, Lausanne, 1896.
[ bib ]
[322] Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., 1988.
[ bib ]
[323] Martin Pelikan, David E. Goldberg, and Erick Cantú-Paz. BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, volume I, pages 525-532. Morgan Kaufmann, 1999.
[ bib | .html ]
[324] Carlos Andrés Pena-Reyes and Moshe Sipper. Fuzzy coco: A cooperative coevolutionary approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems, 9(5):727-737, October 2001.
[ bib ]
[325] Anselmo Pérez. Una introducci´on a la computaci´on evolutiva. http://bioinfo.cpgei.cefetpr.br/mirrors/encore/EA/papers/intro-spanish.ps.gz.
[ bib ]
[326] B. Pfahringer. Controlling constructive induction in CIPF: An MDL approach. In Proc. of the European Conference on Machine Learning ECML-94, volume 784 of LNAI, pages 242-256. Springer-Verlag, 1994.
[ bib ]
[327] Bernhard Pfahringer. Practical Uses of the Minimum Description Length Principle in Inductive Learning. PhD thesis, Institut fur Med.Kybernetik u. AI, Technische Universitat Wien, 1995.
[ bib ]
[328] John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods: support vector learning, pages 185-208. MIT Press, 1999.
[ bib ]
[329] G. Pollastri, P. Baldi, P. Fariselli, and R. Casadio. Improved prediction of the number of residue contacts in proteins by recurrent neural networks. Bioinformatics, 17:S234-S242, 2001.
[ bib ]
[330] G. Pollastri, P. Baldi, P. Fariselli, and R. Casadio. Prediction of coordination number and relative solvent accessibility in proteins. Proteins, 47:142-153, 2002.
[ bib ]
[331] Dean Pomerleau. Neural network perception for mobile robot guidance. Kluwer Academic Publishing, 1993.
[ bib ]
[332] Franco P. Preparata and Michael Ian Shamos. Computational Geometry : An Introduction. Texts and monographs in computer science. Springer-Verlag, 1985.
[ bib ]
[333] Marco Punta and Burkhard Rost. Profcon: novel prediction of long-range contacts. Bioinformatics, 21:2960?2968, 2005.
[ bib ]
[334] Sanbo Qin, Yun He, and Xian-Ming Pan. Predicting protein secondary structure and solvent accessibility with an improved multiple linear regression method. Proteins, 61:473-480, 2005.
[ bib ]
[335] J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, volume 667, pages 3-20. Springer-Verlag, 1993.
[ bib ]
[336] J. R. Quinlan and R. L. Rivest. Inferring decision trees using the minimum description length principle. Information and Computation, 80(3):227-248, 1989.
[ bib ]
[337] J. Ross Quinlan. Induction of decision trees. Machine Learning, 1:81 - 106, 1986.
[ bib ]
[338] J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
[ bib ]
[339] J. Ross Quinlan. Mdl and categorical theories (continued). In Proceedings of the 12th International Conference on Machine Learning, pages 464-470. Morgan Kaufmann, 1995.
[ bib ]
[340] Nicholas J. Radcliffe. Genetic neural networks on MIMD computers. PhD thesis, University of Edinburgh, 1990.
[ bib ]
[341] I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Fromman-Holzboog Verlag, Stuttgart, 1973.
[ bib ]
[342] C.J. Richardson and D.J. Barlow. The bottom line for prediction of residue solvent accessibility. Protein Eng, 12:1051-1054, 1999.
[ bib ]
[343] José C. Riquelme and Jesús S. Aguilar. Codificación indexada de atributos continuos para algoritmos evolutivos en aprendizaje supervisado. In Proceedings of the ``Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados (AEB'02)'', pages 161-167, 2002.
[ bib ]
[344] J. Rissanen. Modeling by shortest data description. Automatica, vol. 14:465-471, 1978.
[ bib ]
[345] Ronald L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.
[ bib | .html ]
[346] Burkhard Rost and Chris Sander. Conservation and prediction of solvent accessibility in protein families. Proteins, 20:216-226, 1994.
[ bib ]
[347] P. Roth. Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47(3):537-560, 1994.
[ bib ]
[348] Günter Rudolph. Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon. Fundamenta Informaticae, 35(1-4):67-89, 1998.
[ bib ]
[349] Roberto Ruiz. New heuristics in feature selection for high dimensional data. AI Commun., 20(2):129-131, 2007.
[ bib ]
[350] S.J. Russel and P. Norvig. Artificial Intelligence, A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 1995.
[ bib ]
[351] M. Salamó and E. Golobardes. Deleting and Building Sort Out Techniques for Case Base Maintenance. In Advances in Case-Based Reasoning. 6th. European Conference on Case-Based Reasoning, pages 365-379. Springer-Verlag, 2002.
[ bib ]
[352] M. Salamó and E. Golobardes. Hybrid deletion policies for case base maintenance. In Proceedings of FLAIRS-2003, pages 150-154. AAAI Press, 2003.
[ bib ]
[353] C. Sander and R. Schneider. Database of homology-derived protein structures. Proteins, 9:56-68, 1991.
[ bib ]
[354] C. Schaffer. Selecting a Classification Method by Cross-Validation. In Fourth Intl. Workshop on Artificial Intelligence & Statistics, pages 15-25, 1993.
[ bib | .html ]
[355] C. Schumacher, M. D. Vose, and L. D. Whitley. The no free lunch and problem description length. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001, pages 565-570. Morgan Kaufmann, 2001.
[ bib ]
[356] J. P. Shaffer. Multiple hypothesis testing. Annu. Rev. Psychol., 46:561-584, 1995.
[ bib ]
[357] C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. University of Illinois Press, Urbana, 1949.
[ bib ]
[358] Y. Shao and C. Bystroff. Predicting interresidue contacts using templates and pathways. Proteins, 53:497-502, 2003.
[ bib ]
[359] Peter K. Sharpe and Robin P. Glover. Efficient ga based techniques for classification. Applied Intelligence, 11(3):277-284, 1999.
[ bib ]
[360] A Shrake and JA. Rupley. Environment and exposure to solvent of protein atoms. lysozyme and insulin. J Mol Biol, 79:351-371, 1973.
[ bib ]
[361] B. Sierra, E. Lazkano, I. Inza, M. Merino, P. Larrañaga, and J. Quiroga. Prototype selection and feature subset selection by estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS. Lecture Notes in Computer Science, 2101:20-29, 2001.
[ bib | .html ]
[362] M.S. Singer, G. Vriend, and R.P. Bywater. Prediction of protein residue contacts with a pdb-derived likelihood matrix. Protein Eng, 15:721-725, 2002.
[ bib ]
[363] David B. Skalak. Prototype and feature selection by sampling and random mutation hill climbing algorithms. In International Conference on Machine Learning, pages 293-301. Morgan Kaufmann, 1994.
[ bib | .html ]
[364] R. E. Smith, A. El-Fallah, B. Ravichandran, R. Mehra, and B. A. Dike. The fighter aircraft lcs: A real-world, machine innovation application. In Larry Bull, editor, Applications of Learning Classifier Systems, pages 113-142. Springer-Verlag, 2004.
[ bib ]
[365] R. E. Smith and Max Kun Jiang. Milcs: a mutual information learning classifier system. In GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation, pages 2945-2952. ACM Press, 2007.
[ bib ]
[366] R. E. Smith and Max Kun Jiang. A learning classifier system with mutual-information-based fitness. Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pages 2173-2180, 25-28 Sept. 2007.
[ bib ]
[367] S.F. Smith. A Learning System Based on Genetic Algorithms. PhD thesis, University of Pittsburgh, 1980.
[ bib ]
[368] Stephen F. Smith. Flexible learning of problem solving heuristics through adaptive search. In Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pages 421-425, Los Altos, CA, 1983. Morgan Kaufmann.
[ bib ]
[369] A. D. Solis and S. Rackovsky. Optimized representations and maximal information in proteins. Proteins, 38(2):149-164, February 2000.
[ bib | http ]

Keywords: no-tag
[370] A. D. Solis and S. Rackovsky. Optimally informative backbone structural propensities in proteins. Proteins, 48:463-486, 2002.
[ bib ]
[371] Armando D Solis and S Rackovsky. Improvement of statistical potentials and threading score functions using information maximization. Proteins, 62(4):892-908, 2006.
[ bib ]
[372] Dong Song, Malcolm I. Heywood, and A. Nur Zincir-Heywood. Training genetic programming on half a million patterns: an example from anomaly detection. IEEE Trans. Evolutionary Computation, 9(3):225-239, 2005.
[ bib ]
[373] Terence Soule and James A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4):293-309, Winter 1998.
[ bib | .html ]
[374] C. Stone and L. Bull. For real! XCS with continuous-valued inputs. Evolutionary Computation Journal, 11(3):298-336, 2003.
[ bib ]
[375] M. Stout, J. Bacardit, J. D. Hirst, J Blazewicz, and N Krasnogor. Prediction of residue exposure and contact number for simplified hp lattice model proteins using learning classifier systems. In Applied Artificial Intelligence, pages 601-608, Genova, Italy, 2006. World Scientific.
[ bib ]
[376] M. Stout, J. Bacardit, J. D. Hirst, and N Krasnogor. Prediction of recursive convex hull class assignments for protein residues. Bioinformatics, 24(7):916-923, 2008.
[ bib ]
[377] M. Stout, J. Bacardit, J.D. Hirst, N. Krasnogor, and J. Blazewicz. From hp lattice models to real proteins: coordination number prediction using learning classifier systems. In 4th European Workshop on Evolutionary Computation and Machine Learning in Bioinformatics 2006, 2006.
[ bib ]
[378] Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Natalio Krasnogor, and Jacek Blazewicz. From hp lattice models to real proteins: Coordination number prediction using learning classifier systems. In Applications of Evolutionary Computing, EvoWorkshops 2006, pages 208-220. Springer LNCS 3907, 2006.
[ bib ]
[379] Michael Stout, Jaume Bacardit, Jonathan D. Hirst, Robert E. Smith, and Natalio Krasnogor. Prediction of topological contacts in proteins using learning classifier systems. Soft Computing, 13:245-258, 2009.
[ bib ]
[380] Mike Stout, Jaume Bacardit, Jonathan D. Hirst, and Natalio Krasnogor. Prediction of residue exposure and contact number for simplified hp lattice model proteins using learning classifier systems. In D. a. Ruan, P. D'hondt, P. F. Fantoni, M. D. Cock, M. Nachtegael, and E. E. Kerre, editors, Proceedings of the 7th International FLINS Conference on Applied Artificial Intelligence, pages 601-608, Genova, Italy, 29-31 August 2006. World Scientific.
[ bib ]
[381] Gilbert Syswerda. Uniform crossover in genetic algorithms. In Proceedings of the third international conference on Genetic algorithms, pages 2-9. Morgan Kaufmann Publishers Inc., 1989.
[ bib ]
[382] Kreangsak Tamee, Larry Bull, and Ouen Pinngern. Towards clustering with XCS. In GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1854-1860. ACM Press, 2007.
[ bib ]
[383] The Authors. The title, year xxxx.
[ bib ]
[384] Haleh Vafaie and Kenneth A. De Jong. Genetic algorithms as a tool for feature selection in machine learning. In Proceeding of the 4th International Conference on Tools with Artificial Intelligence, pages 200-203, 1992.
[ bib ]
[385] L. G. Valiant. A theory of the learnable. Commun. ACM, 27(11):1134-1142, 1984.
[ bib ]
[386] various authors. Special issue on integrating multiple learned models. Machine Learning, 36(1-2), 1999.
[ bib ]
[387] various authors. Special issue on learning classifier systems. Evolutionary Computation Journal, 11(3), 2003.
[ bib ]
[388] William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer-Verlag, 2002. ISBN 0-387-95457-0.
[ bib | http ]
[389] G. Venturini. SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts. In P. B. Brazdil, editor, Machine Learning: ECML-93 - Proc. of the European Conference on Machine Learning, pages 280-296. Springer-Verlag, 1993.
[ bib ]

Keywords:
[390] David Vernet, Maria Salam´o, Carles Vallespí, Joan Camps, Elisabet Golobardes, and Jaume Bacardit. ¿c´omo predecir la evoluci´on del alumno? In Proceedings de las VII Jornadas sobre Enseñazas Universitarias de Informática, pages 335-340, 2001.
[ bib ]
[391] Michael D. Vose. The Simple Genetic Algorithm : Foundations and Theory. Complex Adaptative Systems. Bradford Books, 1999.
[ bib ]
[392] Jun Wang and Wei Wang. A computational approach to simplifying the protein folding alphabet. Nature Structural Biology, 6:1033-1038, 1999.
[ bib ]
[393] Jung-Ying Wang, Hahn-Ming Lee, and Shandar Ahmad. Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression. Proteins, 61:481-491, 2005.
[ bib ]
[394] Ke Wang and Bing Liu. Concurrent discretization of multiple attributes. In Proceedings of the 5th Pacific Rim International Conference on Topics in Artificial Intelligence (PRICAI-98), pages 250-259. LNAI 1531, Springer-Verlag, 1998.
[ bib ]
[395] D. Randall Wilson and Tony R. Martinez. Reduction techniques for instance-based learning algorithms. Machine Learning, 38(3):257-286, 2000.
[ bib | .html ]
[396] Steward W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
[ bib ]
[397] Stewart W. Wilson. Generalization in the XCS classifier system. In John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 665-674, University of Wisconsin, Madison, Wisconsin, USA, 22-25 1998. Morgan Kaufmann.
[ bib | .html ]

Keywords: LCS XCS
[398] Stewart W. Wilson. Get real! XCS with continuous-valued inputs. In L. Booker, Stephanie Forrest, M. Mitchell, and Rick L. Riolo, editors, Festschrift in Honor of John H. Holland, pages 111-121. Center for the Study of Complex Systems, 1999.
[ bib | .html ]
[399] Stewart W. Wilson. Classifiers that approximate functions. Natural Computing: an international journal, 1(2-3):211-234, 2002.
[ bib ]
[400] Stewart W. Wilson. Compact rulesets from XCSI. In Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems, pages 197-210. Springer-Verlag, 2002.
[ bib ]
[401] Ian H. Witten and Eibe Frank. Data Mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, 2000.
[ bib ]
[402] Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
[ bib ]
[403] WH Wolberg and OL Mangasarian. Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology. PNAS, 87(23):9193-9196, 1990.
[ bib ]
[404] David H. Wolpert and William G. Macready. No free lunch theorems for search. Working Papers 95-02-010, Santa Fe Institute, Feb 1995. available at http://ideas.repec.org/p/wop/safiwp/95-02-010.html.
[ bib ]
[405] D.H. Wolpert and D.R. Wolf. Estimating function of probability distributions from a finite set of samples. Phys Rev E, 52:6841-6854, 1995.
[ bib ]
[406] M. L. Wong, W. Lam, and K. S. Leung. Using evolutionary programming and minimum description length principle for data mining of bayesian networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(2):174-178, 1999.
[ bib ]
[407] Matthew J. Wood and Jonathan D. Hirst. Protein secondary structure prediction with dihedral angles. Proteins, 59:476 - 481, 2005.
[ bib ]
[408] James O. Wrabl and Nick V. Grishin. Grouping of amino acid types and extraction of amino acid properties from multiple sequence alignments using variance maximization. Proteins, 61:523?534, 2005.
[ bib ]
[409] D. Wyatt and L. Bull. A memetic learning classifier system for describing continuous-valued problem spaces. In Recent Advances in Memetic Algorithms, pages 355-396. Springer, 2004.
[ bib ]
[410] Jihoon Yang and Vasant G. Honavar. Feature subset selection using a genetic algorithm. IEEE Intelligent Systems, 13(2):44-49, 1998.
[ bib ]
[411] Y. Yang and G. I. Webb. Non-disjoint discretization for naive-bayes classifiers. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML '02), pages 666- 673, San Francisco, 2002. Morgan Kaufmann.
[ bib ]

Keywords: Discretization for Naive Bayes
[412] Kaizhi Yue, Klaus M. Fiebig, Paul D. Thomas, Chan Hue Sun, Eugene I. Shakhnovich, and Ken A. Dill. A test of lattice protein folding algorithms. Proc. Natl. Acad. Sci. USA, 92:325-329, 1995.
[ bib ]
[413] L. Zadeh. Information and control, 1965. L. Zadeh. Fuzzy Sets. Information and Control, 8:338-353, 1965.
[ bib ]
[414] B. Zhang and H. Mühlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3(1):17-38, 1995.
[ bib ]
[415] Ying Zhao and George Karypis. Prediction of contact maps using support vector machines. In Proceedings of the IEEE Symposium on BioInformatics and BioEngineering, pages 26-36, 2003.
[ bib ]
This file has been generated by bibtex2html 1.66