Links between complex networks and combinatorial optimization

Recent results in combinatorial optimization have shown that complex networks can be fruitfully used to modeling problem structure. By conveying results and tools from complex networks to combinatorial optimization, it is possible to achieve a deeper understanding of algorithm behavior. Moreover, some features of the network that models an instance can guide the design of specific heuristics and enable to choose the best solver among a portfolio of algorithms. This work gives a brief overview of the state of the art in this interdisciplinary research field

Symmetry-Breaking and Local Search: A Case Study

Symmetry-breaking has been proved to be very effective when combined with complete solvers. Conversely, it has been conjectured that the use of symmetry-breaking constraints has negative effect on local search-based solvers. This work presents an attempt to model the effect of symmetry-breaking on the search landscape explored by local search. The results, on the one hand, exclude that symmetry-breaking constraints negatively affect the topology of the search space. On the other hand, they strongly suggest that symmetry-breaking perturbs the configuration of local and global optima basins of attraction, making global optima more difficult to be reached

Ant Colony Optimization

Review of the book "Ant colony optimization", by Marco Dorigo and Thomas Stuetzle and published by the MIT Press

LSCS 2006 - Third International Workshop on Local Search Techniques in Constraint Satisfaction

LSCS is an annual workshop devoted to local search techniques in constraint satisfaction. This workshop focuses on all aspects of local search techniques, including: design and implementation of new algorithms, hybrid stochastic-systematic search, winning heuristics, modeling for local-search, global constraints, flexibility and robustness, learning methods, and specific applications. The workshop will provide an informal environment for discussions about recent results in these and related areas. It is open to all members of the CP community. Organized as a satellite event of CP2006 - Twelfth International Conference on Principles and Practice of Constraint Programmin

LSCS 2005 - Second International Workshop on Local Search Techniques in Constraint Satisfaction

LSCS is an annual workshop devoted to local search techniques in constraint satisfaction. This workshop focuses on all aspects of local search techniques, including: design and implementation of new algorithms, hybrid stochastic-systematic search, winning heuristics, modeling for local-search, global constraints, learning methods, and specific applications. The workshop will provide an informal environment for discussions about recent results in these and related areas. It is open to all members of the CP community. Organized as a satellite event of CP2005 - Eleventh International Conference on Principles and Practice of Constraint Programming

LSCS 2007 - Fourth International Workshop on Local Search Techniques in Constraint Satisfaction

LSCS is an annual workshop devoted to local search techniques in constraint satisfaction. This workshop focuses on all aspects of local search techniques, including: design and implementation of new algorithms, hybrid stochastic-systematic search, winning heuristics, modeling for local-search, global constraints, learning methods, and specific applications. The workshop will provide an informal environment for discussions about recent results in these and related areas. It is open to all members of the CP community. Organized as a satellite event of CP2007 - International Conference on Principles and Practice of Constraint Programming

Inernational workshop on Hybrid metaheuristics - HM2004

Workshop on Hybrid metaheuristics, held as a satellite event of ECAI 2004 - Valencia, August 200

Hybrid Metaheuristics: Editorial to the special issue

Combinations of metaheuristic components with components from other metaheuristics or optimization strategies from AI and OR are called hybrid metaheuristics. The design and implementation of hybrid metaheuristics raises problems going beyond questions about the design of a single metaheuristic. Choice and tuning of parameters is for example rendered more difficult by the problem of how to achieve a proper interaction of different algorithm components. Interaction can take place at low-level, using functions from different metaheuristics, or at high-level, e.g., using a portfolio of metaheuristics for automated hybridization. This special issue of the Journal of Mathematical Modelling and Algorithms is devoted to this interdisciplinary topic and contains six papers covering a wide spectrum of subjects

Metaheuristics for the Haplotype Inference Problem: a preliminary analysis

Haplotype inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony criterion) and to solve it using off-the-shelf combinatorial optimization techniques. The main methods applied to Haplotype inference are either simple greedy heuristic or exact methods (Integer Linear/Quadratic Programming, SAT encoding) that, at present, are adequate only for moderate size instances. We believe that metaheuristic and hybrid approaches could provide a better scalability. Moreover, metaheuristics can be very easily combined with problem specific heuristics and they can also be integrated with tree-based search tecnhiques, thus providing a promising framework for hybrid systems in which a good trade-off between effectiveness and efficiency can be reached. In this paper we illustrate a feasibility study of the approach and discuss some relevant design issues, such as modelling and design of approximate solvers which combine constructive heuristics, local search-based improvement strategies and learning mechanisms. Besides the relevance of the Haplotype inference problem itself, this preliminary analysis is also an interesting case study because the formulation of the problem poses some challenges in modelling and hybrid metaheuristic solver design that can be generalized to other problems