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Performance analysis of niching algorithms based on derandomized-ES variants

Published: 07 July 2007 Publication History

Abstract

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 +<over>, 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.

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  • (2011)Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-artSwarm and Evolutionary Computation10.1016/j.swevo.2011.05.0051:2(71-88)Online publication date: Jun-2011
  • (2010)A niching particle swarm segmentation of infrared images2010 Sixth International Conference on Natural Computation10.1109/ICNC.2010.5583389(3739-3742)Online publication date: Aug-2010
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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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Author Tags

  1. MPR analysis
  2. derandomized evolution strategies
  3. niching

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2017)Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling SubpopulationsEvolutionary Computation10.1162/evco_a_0018225:3(439-471)Online publication date: Sep-2017
  • (2011)Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-artSwarm and Evolutionary Computation10.1016/j.swevo.2011.05.0051:2(71-88)Online publication date: Jun-2011
  • (2010)A niching particle swarm segmentation of infrared images2010 Sixth International Conference on Natural Computation10.1109/ICNC.2010.5583389(3739-3742)Online publication date: Aug-2010
  • (2007)Self-Adaptive Niching CMA-ES with Mahalanobis Metric2007 IEEE Congress on Evolutionary Computation10.1109/CEC.2007.4424555(820-827)Online publication date: Sep-2007

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