Binary and floating-point function optimization using messy genetic algorithms
Binary and floating-point function optimization using messy genetic algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
The Effect of Spin-Flip Symmetry on the Performance of the Simple GA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Selected Papers from AISB Workshop on Evolutionary Computing
Multivariate multi-model approach for globally multimodal problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A clustering-based differential evolution for global optimization
Applied Soft Computing
A preliminary study on EDAs for permutation problems based on marginal-based models
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Effect of topology on diversity of spatially-structured evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A framework for multi-model EDAs with model recombination
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Introducing the mallows model on estimation of distribution algorithms
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
On handling ephemeral resource constraints in evolutionary search
Evolutionary Computation
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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This paper introduces clustering as a tool to improve the effects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is first clustered and the solutions in each of the clusters are processed separately. This alleviates the problem of symmetry which is often a major difficulty of many evolutionary algorithms in combinatorial optimization. Furthermore, it incorporates niching into genetic algorithms and, for the first time, the probabilistic model-building genetic algorithms. The dynamics and performance of the proposed method are illustrated on example problems.