Analyzing design principles for competitive evolution strategies in constrained search spaces

TL;DR

This paper conducts an extensive empirical analysis of the $ ext{ extepsilon}$MAg-ES algorithm for constrained optimization, aiming to understand its success factors and the impact of its components in high-dimensional search spaces.

Contribution

It provides a detailed investigation of the working principles of $ ext{ extepsilon}$MAg-ES, including significance testing to evaluate component contributions and performance insights.

Findings

Identifies key algorithmic components influencing performance

Provides insights into the algorithm's effectiveness in high-dimensional spaces

Uses significance testing to validate performance differences

Abstract

In the context of the 2018 IEEE Congress of Evolutionary Computation, the Matrix Adaptation Evolution Strategy for constrained optimization turned out to be notably successful in the competition on constrained single objective real-parameter optimization. Across all considered instances the so-called $\epsilon$MAg-ES achieved the second rank. However, it can be considered to be the most successful participant in high dimensions. Unfortunately, the competition result does not provide any information about the modus operandi of a successful algorithm or its suitability for problems of a particular shape. To this end, the present paper is concerned with an extensive empirical analysis of the $\epsilon$MAg-ES working principles that is expected to provide insights about the performance contribution of specific algorithmic components. To avoid rankings with respect to insignificant differences within the algorithm realizations, the paper additionally introduces significance testing into the ranking process.