Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
This is an incremental review that addresses the need for standardized benchmarking in constrained optimization for researchers and developers in evolutionary computation.
The paper reviews benchmarking principles and environments for evolutionary algorithms in single-objective real-valued constrained optimization, providing an overview of problem domains and evaluating the merits and demerits of existing frameworks to support algorithm developers.
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.