Constraint Handling

Abstract

In this chapter we return to an issue first introduced in Sect. 1.3, namely that some problems have constraints associated with them. This means that not all possible combinations of variable values represent valid solutions to the problem at hand, and we examine how this impacts on the design of an evolutionary algorithm. This issue has great practical relevance because many real-world problems are constrained. It is also theoretically challenging, since many intractable problems (NP-hard, NP-complete, etc.) are constrained. Unfortunately, constraint handling is not straightforward in an EA, because the variation operators (mutation and recombination) are typically ‘blind’ to constraints. This means that even if the parents satisfy some constraints, there is no guarantee their offspring will. This chapter reviews the most commonly used techniques for constraint handling, identifies a number of common features, and provides some guidance for the algorithm designer.