Mapping constrained optimization problems to penalty parameters: An empirical study

Penalty function method is one of the most popular used Constraint Handling Techniques for Evolutionary Algorithms (EAs) solution selecting, whose performance is mainly determined by penalty parameters. This paper tries to study the penalty parameter from the aspect of problem characteristics, i.e., to construct a corresponding relationship between the problems and the penalty parameters. The experimental results confirm the relationship, which provides valuable reference for future algorithm design.