Evaluation of Crossover Operator Performance in Genetic Algorithms with Binary Representation
Stjepan Picek, Marin Golub, Domagoj Jakobović · 2011
Abstract. 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 im-portant role as an analogue to reproduction in biological sense. During the last decades, a number of different crossover operators have been suc-cessfully designed. However, systematic comparison of those operators is difficult to find. This paper presents a comparison of 10 crossover oper-ators that are used in genetic algorithms with binary representation. To achieve this, experiments are conducted on a set of 15 optimization prob-lems. 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. 1