On Restricting Real-Valued Genotypes in Evolutionary Algorithms

TL;DR

This paper addresses the challenge of effectively limiting real-valued genotypes in Evolutionary Algorithms, proposing a method that improves theoretical properties and benchmark performance with minimal intervention.

Contribution

It introduces a novel method for constraining real-valued genotypes that enhances algorithm robustness and performance, supported by empirical and benchmark evaluations.

Findings

The proposed method outperforms existing approaches in benchmark tests.

It maintains stability under repeated variation operations.

The method requires minimal user intervention.

Abstract

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks.