Use of statistical outlier detection method in adaptive evolutionary algorithms

TL;DR

This paper introduces a framework for adapting probabilities of search operators in evolutionary algorithms using statistical outlier detection, showing that outlier-based operator selection improves performance over traditional methods.

Contribution

It proposes a novel outlier-based adaptation method for EA operators and demonstrates its effectiveness across multiple test problems.

Findings

Outlier-based operator selection outperforms average-based methods.

Statistical interpretation significantly impacts EA performance.

Adaptive methods with outlier detection outperform non-adaptive approaches.

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.