Evolving Evolutionary Algorithms using Multi Expression Programming

TL;DR

This paper presents a method to automatically evolve entire evolutionary algorithms using Multi Expression Programming, optimizing their parameters and structure for specific problems, demonstrated through numerical experiments.

Contribution

It introduces evolving complete EAs with MEP, a novel approach for automatic algorithm configuration and design.

Findings

Evolved EAs outperform traditional fixed-parameter EAs.

The approach effectively optimizes EAs for function optimization tasks.

Numerical experiments validate the method's effectiveness.

Abstract

Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the effectiveness of this approach.