Use of statistical outlier detection method in adaptive evolutionary algorithms

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.

• Publication year

  2006

• Venue

  Annual Conference on Genetic and Evolutionary Computation

• Publication date

  2006-07-08

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/1143997.1144205arXiv 0907.0595
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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