Adaptive global optimization with local search
Adaptive global optimization with local search
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Proceedings of the 5th International Conference on Genetic Algorithms
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Random Structures & Algorithms
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Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Complete convergence of message passing algorithms for some satisfiability problems
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality
IEEE Transactions on Information Theory
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A Self-guided Genetic Algorithm for permutation flowshop scheduling problems
Computers and Operations Research
Entropy-based efficiency enhancement techniques for evolutionary algorithms
Information Sciences: an International Journal
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Journal of Heuristics
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Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper presents a local search method for the Bayesian optimization algorithm (BOA) based on the concepts of substructural neighborhoods and loopy belief propagation. The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the topology of the neighborhoods explored in local search. On the other hand, belief propagation in graphical models is employed to find the most suitable configuration of conflicting substructures. The results show that performing loopy substructural local search (SLS) in BOA can dramatically reduce the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.