A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Pareto set and EMOA behavior for simple multimodal multiobjective functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
EA-Powered Basin Number Estimation by Means of Preservation and Exploration
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Adaptive niche radii and niche shapes approaches for niching with the cma-es
Evolutionary Computation
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Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on Evolutionary Computation
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A Review of Niching Genetic Algorithms for Multimodal Function Optimization
Cybernetics and Systems Analysis
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The present paper investigates the hybridization of two well-known multimodal optimization methods, i.e. species conservation and multinational algorithms. The topological species conservation algorithm embraces the vision of the existence of subpopulations around seeds (the best local individuals) and the preservation of these dominating individuals from one generation to another, but detects multimodality by means of the hill-valley mechanism employed by multinational algorithms. The aim is to inherit the strengths of both parent techniques and at the same time overcome their flaws. The species conservation algorithm efficiently keeps track of several good search space regions at once, but is difficult to parametrize without prior problem knowledge. Conversely, the multinational algorithms use many functionevaluations to establish subpopulations, but do not depend onprovided radius parameter values. Experiments with all threealgorithms are made on a wide range of test problems in order toinvestigate their advantages and shortcomings.