Selective crossover in genetic algorithms: An empirical study

Abstract

Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: the crossover operator. The motivation for this study is to provide an adaptive crossover operator that gives best overall performance on a large set of problems. A new adaptive crossover operator "selective crossover" is proposed and is compared with two-point and uniform crossover on a problem generator where epistasis can be varied and on trap functions where deception can be varied. We provide empirical results which show that selective crossover is more efficient than two-point and uniform crossover across a representative set of search problems containing epistasis.

Multi-objective optimization and multi-criteria decision aid applied to the design of 3D-stacked integrated circuits

“…Typically, two children are created from each set of parents. One method of crossover (one-point crossover) will be explained here but other approaches exists such as the two-point crossover or the uniform crossover [56]. The one-point crossover is chosen here as it is one of the best performing operators for various kinds of problem and finding a best overall operator is often difficult [57].…”

Genetic Algorithms for Balanced Spanning Tree Problem

“…In genetic algorithms, determining representation method, population size, selection technique, crossover and mutation probabilities, and stopping criteria are crucial since they mainly affect the convergence of the algorithm (see [1], [13], [18], [19], [20]). …”

“…There are two common crossover techniques: single-point crossover and multipoint crossover. Many researchers studied the influence of crossover approach and crossover probability to the efficiency of the whole genetic algorithm, see for example [17], [20] and the references therein. In this paper, we use a multi-point crossover approach by exchanging a randomly selected set of edges between the two parents.…”

Design of Evolutionary Methods Applied to the Learning of Bayesian Network Structures

“…At least, the operator used has been developed from the model of (Vekaria & Clack, 1998). This operator is used to generate two individuals with the particularity of defining the crossing point as a function of the quality of the individual.…”