A Study of Crossover Operators in Genetic Algorithms

Abstract

AbstractCrossover is an important operator in genetic algorithms. Although hundreds of application dependent and independent crossover operators exist in the literature, this chapter provides holistic, but by no means an exhaustive, overview of different crossover techniques used in different variants of genetic algorithms. We will review some of the commonly used crossover operators in binary-coded genetic algorithms as well as in real-coded genetic algorithms and explore the use cases and performance of different techniques for different applications to provide a better understanding of the types of bias exhibited by different crossover operators. This knowledge can be useful when designing an algorithm for a specific problem, particularly if there are known patterns or dependencies in the selected representation.KeywordsCrossoverGenetic algorithmsEvolutionary programming