Claudio F. Lima

I am a Research Fellow at the University of Nottingham, U.K., working in the Centre for Plant Integrative Biology (CPIB) and the Automated Scheduling, Optimisation and Planning (ASAP) research group. My role is to address automated model synthesis in Systems Biology where the structure of candidate models is reversed engineered from experimental data, using P-systems as the modelling framework. This is a complementary approach to human-designed models developed at CPIB. To search for the appropriate model structure and parameters several state-of-the-art optimization algorithms are being considered. I'm also involved with parameter estimation for models with fixed or know structure and Bayesian inference in gene regulatory networks.

I have a degree in Systems and Computing Engineering from the University of Algarve (Portugal) and a PhD in Computer Science from the same University. During my PhD studies I was also a visiting researcher at the University of Illinois (USA). My PhD research focused on improving the scalability of estimation of distribution algorithms (combination of evolutionary computation with machine learning methods) for solving challenging optimization problems. I have also conducted research on adaptive parameter setting for evolutionary algorithms and dynamic optimization.

Loopy Substructural Local Search for the Bayesian Optimization Algorithm. Workshop on Engineering Stochastic Local Search Algorithms (SLS 2009), Brussels, Belgium, September 2009. Second Best Presentation Award [pdf]

Substructural Local Search in Discrete Estimation of Distribution Algorithms. PhD Defense, University of Algarve, Faro, Portugal, May 2009. [pdf]

From Mating Pool Distributions to Model Overfitting. ACM Genetic and Evolutionary Computation Conference (GECCO-2008), Atlanta, USA. Best Paper Award [pdf]

Investigating Restricted Tournament Replacement in ECGA for Non-Stationary Environments. ACM Genetic and Evolutionary Computation Conference (GECCO-2008), Atlanta, USA. [pdf]

Structural Accuracy of Probabilistic Models in BOA. GRICES Portugal-Brazil meeting (11/2007), Faro, Portugal. [pdf] (extended version)

Structural Accuracy of Probabilistic Models in BOA. Workshop on Optimization by Building and Using Probabilistic Models (OBUPM-2007), London, UK. [pdf] (shorter version)

Influence of Selection and Replacement Strategies on Linkage Learning in BOA. IEEE Congress on Evolutionary Computation (CEC-2007), Singapore. [pdf]

Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm. Parallel Problem Solving from Nature (PPSN-2006), Reykjavik, Iceland. [pdf]

The Bayesian Optimization Algorithm with Substructural Local Search. Workshop on Optimization by Building and Using Probabilistic Models (OBUPM-2006), Seattle, USA. [pdf]

Combining Competent Crossover and Mutation Operators: a Probabilistic Model Building Approach. ACM Genetic and Evolutionary Computation Conference (GECCO-2005), Washington DC, USA. [pdf]

Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search. Genetic and Evolutionary Computation Conference (GECCO-2004), Seattle, USA. [pdf]

2010

F. Lobo, C. Lima (2010). Towards Automated Selection of Estimation of Distribution Algorithms. Workshop on Optimization by Building and Using Probabilistic Models (OBUPM-2010), pag. 1945-1952, ACM Press.

C. Garcia-Martinez, C. Lima, J. Twycross, N. Krasnogor, M. Lozano (2010). P System Model Optimization by Means of Evolutionary Based Search Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2010), pag. 187-194, ACM Press. (Nominated for Best Paper Award)

C. F. Lima, F. G. Lobo, M. Pelikan, D. E. Goldberg (2010). Model Accuracy in the Bayesian Optimization Algorithm. IlliGAL Technical Report No. 2010002, University of Illinois at Urbana-Champaign. [pdf]

2009

T.-L. Yu, D. E. Goldberg, K. Sastry, C. F. Lima, and M. Pelikan (2009). Dependency Structure Matrix, Genetic Algorithms, and Effective Recombination. Evolutionary Computation Journal, Vol. 17, No. 4, pag. 595-626, MIT Press.

M. Hauschild, M. Pelikan, K. Sastry, and C. F. Lima (2009). Analyzing Probabilistic Models in Hierarchical BOA. IEEE Transactions on Evolutionary Computation, Vol. 13, Issue 6, pag. 1199-1217, IEEE Press.

C. M. Fernandes, C. F. Lima, J.L.J. Laredo, A.C. Rosa, and J.J. Merelo (2009). An Ant-based Rule for UMDA’s Update Strategy. Proceedings of the 10th European Congress on Artificial Life, Lecture Notes In Computer Science, Springer.

C. F. Lima, M. Pelikan, F. G. Lobo, and D. E. Goldberg (2009). Loopy Substructural Local Search for the Bayesian Optimization Algorithm. Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms (SLS-2009), Lecture Notes In Computer Science Vol. 5752, pag. 61-75, Springer.

C. F. Lima (2009). Substructural Local Search in Discrete Estimation of Distribution Algorithms. PhD Thesis, University of Algarve, Faro, Portugal. [pdf]

2008

C. F. Lima, F. G. Lobo, and M. Pelikan (2008). From Mating Pool Distributions to Model Overfitting. In M. Keijzer et al. (Eds.), Proceedings of the ACM Genetic and Evolutionary Computation COnference (GECCO-2008), pp. 431-438, ACM. Best Paper Award, Estimation of Distribution Algorithms Track.

C. F. Lima, C. Fernandes, and F. G. Lobo (2008). Investigating Restricted Tournament Replacement in ECGA for Non-Stationary Environments. In M. Keijzer et al. (Eds.), Proceedings of the ACM Genetic and Evolutionary Computation COnference (GECCO-2008), pp. 439-446, ACM. Also as IlliGAL Technical Report No. 2008010, University of Illinois at Urbana-Champaign. [pdf]

C. Fernandes, C. F. Lima, and A. Rosa (2008). UMDAs for Dynamic Optimization Problems. In M. Keijzer et al. (Eds.), Proceedings of the ACM Genetic and Evolutionary Computation COnference (GECCO-2008), pp. 399-406, ACM.

X. LLora, K. Sastry, C. F. Lima, F. G. Lobo, and D. E. Goldberg (2008). Linkage Learning, Rule Representation, and the X-ary Extended Compact Classifier System. Learning Classifier Systems, Lecture Notes In Computer Science Vol. 4998, pag. 189-205, Springer. [pdf]

C. F. Lima, M. Pelikan, D. E. Goldberg, F. G. Lobo, K. Sastry, and M. Hauschild (in press). Linkage Learning Accuracy in the Bayesian Optimization Algorithm. In Y.-P. Chen et al. (Eds.), Linkage in Evolutionary Computation, Springer.

2007

C. F. Lima, M. Pelikan, D. E. Goldberg, F. G. Lobo, K. Sastry, and M. Hauschild (2007). Influence of Selection and Replacement Strategies on Linkage Learning in BOA. Proceedings of the IEEE Congress on Evolutionary Computation (CEC-2007), pp. 1083-1090, IEEE Press. Also as IlliGAL Technical Report No. 2007013, University of Illinois at Urbana-Champaign. [pdf]

M. Hauschild, M. Pelikan, K. Sastry, and C. F. Lima (2007). Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin Glasses. In D. Thierens et al. (Eds.), Proceedings of the ACM Genetic and Evolutionary Computation COnference (GECCO-2007), pp. 1233-1240. Also as MEDAL Technical Report No. 2007001. [pdf]

F. G. Lobo and C. F. Lima (2007). Adaptive Population Sizing Schemes in Genetic Algorithms. In F. G. Lobo et al. (Eds.), Parameter Setting in Evolutionary Algorithms, Studies in Computational Intelligence Series, Springer.

F. G. Lobo, C. F. Lima, and Z. Michalewicz (Eds.) (2007). Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence Series, Springer. [Table of Contents] [Preface] [Order]

2006

C. F. Lima, M. Pelikan, K. Sastry, M. Butz, D. E. Goldberg, F. G. Lobo (2006). Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm. In T. P. Runarsson et. al. (Eds.), PPSN IX: Parallel Problem Solving from Nature, LNCS 4193, pp. 232-241. Springer. Also as IlliGAL Technical Report No. 2006021. [pdf]

F. G. Lobo and C. F. Lima (2006). On the Utility of the Multimodal Problem Generator for Assessing the Performance of Evolutionary Algorithms. In M. Keijzer et al. (Eds.), Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation COnference (GECCO-2006), pp. 1233-1240. ACM Press. Also as UALG-ILab Technical Report No. 200601. [pdf]

F. G. Lobo and C. F. Lima (2006). Revisiting Evolutionary Algorithms with On-The-Fly Population Size Adjustment. In M. Keijzer et al. (Eds.), Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation COnference (GECCO-2006), pp. 1241-1248. ACM Press. Also as UALG-ILab Technical Report No. 200602. [pdf]

K. Sastry, C. F. Lima, and D. E. Goldberg (2006). Evaluation Relaxation using Substructural Information and Linear Estimation. In M. Keijzer et al. (Eds.), Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation COnference (GECCO-2006), pp. 419-426. ACM Press. Also as IlliGAL Technical Report No. 2006003. [pdf]

K. Sastry, P. Winward, D. E. Goldberg, and C. F. Lima (2006). Fluctuating Crosstalk as a Source of Deterministic Noise and its Effect on GA Scalability. In F. Rothlauf et al. (Eds.), Applications of Evolutionary Computing - EvoWorkshops 2006, LNCS 3907, pp. 740-751, Springer. Also as IlliGAL Technical Report No. 2005025. [pdf]

2005

C. F. Lima, K. Sastry, D. E. Goldberg, and F. G. Lobo (2005). Combining Competent Crossover and Mutation Operators: a Probabilistic Model Building Approach. In H. Beyer et al. (Eds.), Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation COnference (GECCO-2005), pp. 735-742, ACM Press. Also as IlliGAL Technical Report No. 2005002. [pdf]

F. G. Lobo and C. F. Lima (2005). A Review of Adaptive Population Sizing Schemes in Genetic Algorithms. In F. G. Lobo and C. F. Lima (Eds.), Proceedings of the 2005 Workshop on Parameter Setting in Genetic and Evolutionary Algorithms (PSGEA-2005), part of GECCO 2005. ACM Press, 2005.

F. G. Lobo and C. F. Lima (Eds.) (2005). Proceedings of the 2005 Workshop on Parameter Setting in Genetic and Evolutionary Algorithms (PSGEA-2005), part of GECCO 2005. ACM Press. [proceedings] [webpage]

F. G. Lobo, C. F. Lima, and H. Martires (2005). Massive Parallelization of the Compact Genetic Algorithm. In R. Ribeiro et al. (Eds.), Proceedings of the International Conference on Adaptive and Natural computiNG Algorithms (ICANNGA-2005), pp. 530-533. Springer.

2004

F. G. Lobo, C. F. Lima, and H. Martires (2004). An Architecture for Massive Parallelization of the Compact Genetic Algorithm. In K. Deb et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Part II, LNCS 3103, pp. 412-413. Springer.

C. F. Lima and F. G. Lobo (2004). Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search. In K. Deb et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Part I, LNCS 3102, pp. 1328-1339. Springer.

2003

C. F. Lima (2003). Uma Abordagem Evolutiva na Construcção de um Algoritmo de Optimização Robusto e de Fácil Utilização. Engineering Degree Thesis, Systems and Computation Engineering, University of Algarve. (in Portuguese)

Substructural Local Search in Discrete Estimation of Distribution Algorithms [pdf]

Abstract: The last decade has seen the rise and consolidation of a new trend of stochastic optimizers known as estimation of distribution algorithms (EDAs). In essence, EDAs build probabilistic models of promising solutions and sample from the corresponding probability distributions to obtain new solutions. This approach has brought a new view to evolutionary computation because, while solving a given problem with an EDA, the user has access to a set of models that reveal probabilistic dependencies between variables, an important source of information about the problem.

This dissertation proposes the integration of substructural local search (SLS) in EDAs to speedup the convergence to optimal solutions. Substructural neighborhoods are defined by the structure of the probabilistic models used in EDAs, generating adaptive neighborhoods capable of automatic discovery and exploitation of problem regularities. Specifically, the thesis focuses on the extended compact genetic algorithm and the Bayesian optimization algorithm. The utility of SLS in EDAs is investigated for a number of boundedly difficult problems with modularity, overlapping, and hierarchy, while considering important aspects such as scaling and noise. The results show that SLS can substantially reduce the number of function evaluations required to solve some of these problems. More importantly, the speedups obtained can scale up to the square root of the problem size.

Collaborators/Co-authors:

Fernando G. Lobo, University of Algarve, Portugal
David E. Goldberg, University of Illinois at Urbana-Champaign, USA
Martin Pelikan, University of Missouri at St. Louis, USA
Kumara Sastry, Intel Corporation, USA
Martin Butz, University of Wurzburg, Germany
Mark Hauschild, University of Missouri at St. Louis, USA
Carlos Fernandes, University of Granada, Spain
Tian-Li Yu, National Taiwan University, Taiwan
Xavier Llora, National Center for Supercomputing Applications, USA

Related Research Labs:

Illinois Genetic Algorithms LAboratory (IlliGAL)
Missouri Estimation of Distribution Algorithms Laboratory (MEDAL)
Intelligent Systems Group