Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review

TL;DR

This paper critically reviews the principles and current environments used for benchmarking evolutionary algorithms in constrained optimization, providing insights into their strengths and weaknesses.

Contribution

It offers a comprehensive analysis of existing benchmark environments for constrained optimization and discusses their alignment with fundamental benchmarking principles.

Findings

Review of current benchmark environments

Analysis of their strengths and limitations

Guidelines for selecting appropriate benchmarks

Abstract

Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presents common principles that need to be taken into account when considering benchmarking problems for constrained optimization. Current benchmark environments for testing Evolutionary Algorithms are reviewed in the light of these principles. Along with this line, the reader is provided with an overview of the available problem domains in the field of constrained benchmarking. Hence, the review supports algorithms developers with information about the merits and demerits of the available frameworks.