On the Efficiency of Crossover Operators in Genetic Algorithms with Binary Representation

Genetic Algorithm (GA) represents robust, adaptive method successfully applied to various optimization problems. To evaluate the performance of the genetic algorithm, it is common to use some kind of test functions. However. the "no free lunch" theorem states it is not possible to find the perfect, universal solver algorithm. To evaluate the algorithm, it is necessary to characterize the type of problems for which that algorithm is suitable. That would allow conclusions about the performance of the algorithm based on the class of a problem. In performance of a genetic algorithm, crossover operator has an invaluable role. To better understand performance of a genetic algorithm in a whole, it is necessary to understand the role of the crossover operator. The purpose of this paper is to compare larger set of crossover operators on the same test problems and evaluate their's efficiency. Results presented here confirm that uniform and two-point crossover operators give the best results but also show some interesting comparisons between less used crossover operators like segmented or half-uniform crossover.