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Use of statistical outlier detection method in adaptive evolutionary algorithms

Published: 08 July 2006 Publication History

Abstract

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case.

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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
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: 08 July 2006

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Author Tags

  1. evolutionary algorithm
  2. feedback adaptation
  3. genetic algorithm

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GECCO06
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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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  • (2024)Effective Adaptive Mutation Rates for Program SynthesisProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654135(952-960)Online publication date: 14-Jul-2024
  • (2024)Random clustering-based outlier detectorInformation Sciences: an International Journal10.1016/j.ins.2024.120498667:COnline publication date: 1-May-2024
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