Call for Papers
Journal of Heuristics
Special Issue on ‘Hyper-heuristics in Search and Optimisation’
Manuscript submissions: March 12st, 2009 (EXTENDED)
Notification: July 1st, 2009
Despite the significant progress in building search methodologies for a wide variety of application areas so far, such approaches still require specialists to integrate their expertise in a given problem domain. Many researchers from computer science, artificial intelligence and operational research fields have already acknowledged the need for developing automated systems to replace the role of a human expert in such situations. One of the main ideas for automating the design of heuristics requires the incorporation of learning mechanisms into algorithms to adaptively guide the search. Both learning and adaptation processes can be realised on-line or off-line, and be based on constructive or perturbative heuristics. There is an emerging search and optimization tool in this line of thinking: hyper-heuristics. Hyper-heuristics can be thought of as "heuristics to select/adapt/generate heuristics". They are techniques that explore a search space of heuristics. Therefore, they differ from most applications of meta-heuristics which explore a search space of solutions. There might be multiple heuristics from which one can choose for solving a problem, and each heuristic has its own strength and weakness. The idea is to automatically devise algorithms by combining the strength and compensating the weakness of known heuristics. The approach is motivated by the aim of raising the level of generality at which search systems can operate, with the end goal of obtaining methods applicable to a wider range of problem domains than is possible today.
The aim of this special issue is to reflect the most recent advances in the field, and increase the awareness of the computing community at large on the possibilities of raising the level of generality of search methodologies. Topics of interests include (but are not limited to):
· Hyper-heuristics
o applications and new challenging domains
o classifications or categorisations of approaches
o evolution of heuristics (by genetic programming or grammatical evolution)
o issues in multi-objective, discrete and continuous optimisation
o integration of machine learning techniques (e.g. reinforcement learning, classifier systems, neural networks, and others)
o new frameworks for better utilization of local search components
o scalability issues
o parallel models
· Related approaches
o adaptive and self-tuning algorithms
o adaptive multi-meme algorithms
o algorithm portfolios
o model-based search
o reactive search
Authors should submit their manuscripts using the Journal of Heuristics Editorial Manager at https://www.editorialmanager.com/heur/, please select “Special Issue – Hyperheuristics” as the article type. All manuscripts should be prepared in accordance with the journal's instructions for authors available at the aforementioned website. Each manuscript will go through the journal’s typical strict peer review process to ensure the high quality of this volume.
Please do not hesitate to contact the guest editors for any further inquiries.
Guest Editors:
Gabriela Ochoa and Ender Ozcan
{gxo, exo} ATT cs.nott.ac.uk
Automated Scheduling and Planning (ASAP) research group,