Evaluation of Crossover Operator Performance in Genetic Algorithms with Binary Representation

Genetic algorithms (GAs) generate solutions to optimization problems using techniques inspired by natural evolution, like crossover, selection and mutation. In that process, crossover operator plays an important role as an analogue to reproduction in biological sense. During the last decades, a number of different crossover operators have been successfully designed. However, systematic comparison of those operators is difficult to find. This paper presents a comparison of 10 crossover operators that are used in genetic algorithms with binary representation. To achieve this, experiments are conducted on a set of 15 optimization problems. A thorough statistical analysis is performed on the results of those experiments. The results show significant statistical differences between operators and an overall good performance of uniform, single-point and reduced surrogate crossover. Additionally, our experiments have shown that orthogonal crossover operators perform much poorer on the given problem set and constraints.