Article

The dispersion metric and the CMA evolution strategy

Authors:

Darrell WhitleyAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 477 - 484

https://doi.org/10.1145/1143997.1144085

Published: 08 July 2006 Publication History

Abstract

An algorithm independent metric is introduced that measures the dispersion of a uniform random sample drawn from the top ranked percentiles of the search space. A low dispersion function is one where the dispersion decreases as the sample is restricted to better regions of the search space. A high dispersion function is one where dispersion stay constant or increases as the sample is restricted to better regions of the search space. This distinction can be used to explain why the CMA Evolution Strategy is more efficient on some multimodal problems than on others.

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Cited By

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The dispersion metric and the CMA evolution strategy

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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

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Published: 08 July 2006

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://doi.org/10.3390/info16030190
Engelbrecht AGouldie R(2024)Fitness Landscape Analysis of Product Unit Neural NetworksAlgorithms10.3390/a1706024117:6(241)Online publication date: 4-Jun-2024
https://doi.org/10.3390/a17060241
Long Fvan Stein BFrenzel MKrause PGitterle MBäck T(2024)Generating Cheap Representative Functions for Expensive Automotive Crashworthiness OptimizationACM Transactions on Evolutionary Learning and Optimization10.1145/36465544:2(1-26)Online publication date: 13-Feb-2024
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