Evolutionary Algorithms for Parameter Optimization-Thirty Years Later

Thirty years, 1993-2023, is a huge time frame in science. We address some major developments in the field of evolutionary algorithms, with applications in parameter optimization, over these 30 years. These include the covariance matrix adaptation evolution strategy and some fast-growing fields such as multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. Moreover, we also discuss particle swarm optimization and differential evolution, which did not exist 30 years ago, either. One of the key arguments made in the paper is that we need fewer algorithms, not more, which, however, is the current trend through continuously claiming paradigms from nature that are suggested to be useful as new optimization algorithms. Moreover, we argue that we need proper benchmarking procedures to sort out whether a newly proposed algorithm is useful or not. We also briefly discuss automated algorithm design approaches, including configurable algorithm design frameworks, as the proposed next step toward designing optimization algorithms automatically, rather than by hand.

Automated summary

This article discusses some major developments in the field of evolutionary algorithms over the past 30 years, including covariance matrix adaptation evolution strategy, multimodal optimization, surrogate-assisted optimization, multiobjective optimization, and automated algorithm design. The article emphasizes the need for fewer algorithms and proper benchmarking procedures to determine the usefulness of newly proposed algorithms.