Evolving Evolutionary Algorithms using Linear Genetic Programming

TL;DR

This paper introduces a novel model that uses Linear Genetic Programming to evolve customized Evolutionary Algorithms for various optimization problems, demonstrating competitive performance against standard methods.

Contribution

The paper presents a new LGP-based framework for automatically evolving EAs tailored to specific problems, enhancing adaptability and effectiveness.

Findings

Evolved EAs perform comparably to standard algorithms.

Evolved EAs sometimes outperform traditional approaches.

The model is applicable to diverse optimization problems.

Abstract

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.