Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Proceedings of the 6th International Conference on Genetic Algorithms
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Niching in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
On three new approaches to handle constraints within evolution strategies
Natural Computing: an international journal
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Niching in evolution strategies and its application to laser pulse shaping
EA'05 Proceedings of the 7th international conference on Artificial Evolution
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A survey of niching algorithms, based on 5 variants of derandomized Evolution Strategies (ES), is introduced. This set of niching algorithms, ranging from the very first derandomized approach to self-adaptation of ES to the sophisticated (1 +, λ) Covariance Matrix Adaptation (CMA), is applied to multimodal continuous theoretical test functions, of different levels of difficulty and various dimensions, and compared with the MPR performance analysis tool. While characterizing the performance of the different derandomized variants in the context of niching, some conclusions concerning the niching formation process of the different mechanisms are drawn, and the hypothesis of a tradeoff between learning time and niching acceleration is numerically confirmed. Niching with (1+λ)-CMA core mechanism is shown to experimentally outperform all the other variants. Some theoretical arguments supporting the advantage of a plus-strategy for niching are discussed.