Evolving Evolutionary Algorithms with Patterns

TL;DR

This paper introduces a novel model for evolving Evolutionary Algorithms using Multi Expression Programming, enabling automatic generation of problem-specific algorithms that can outperform traditional human-designed methods.

Contribution

The paper presents a new model based on MEP for evolving EAs, demonstrating its effectiveness in automatically generating competitive algorithms.

Findings

Evolved EAs perform comparably to human-designed GAs on benchmark problems.

The model successfully encodes evolutionary patterns for problem-specific optimization.

Numerical experiments validate the approach's competitiveness.

Abstract

A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern that is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into a standard evolutionary scheme that is used for solving a particular problem. Several evolutionary algorithms for function optimization are evolved by using the considered model. The evolved evolutionary algorithms are compared with a human-designed Genetic Algorithm. Numerical experiments show that the evolved evolutionary algorithms can compete with standard approaches for several well-known benchmarking problems.