Parameter-less Optimization with the Extended Compact Genetic Algorithm and Iterated Local Search

TL;DR

This paper introduces a parameter-less optimization framework combining ECGA and ILS, demonstrating robustness and ease of use on various problems without requiring domain knowledge.

Contribution

It extends the parameter-less genetic algorithm by integrating ECGA and ILS, removing the need for parameter tuning in optimization.

Findings

Robust performance on well-known problems

No domain knowledge required for effective optimization

Simplifies the optimization process by eliminating parameters

Abstract

This paper presents a parameter-less optimization framework that uses the extended compact genetic algorithm (ECGA) and iterated local search (ILS), but is not restricted to these algorithms. The presented optimization algorithm (ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA), where the parameters of a selecto-recombinative GA are eliminated. The approach that we propose is tested on several well known problems. In the absence of domain knowledge, it is shown that ILS+ECGA is a robust and easy-to-use optimization method.