Selective crossover in genetic algorithms: An empirical study

  • Kanta Vekaria
  • Chris Clack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


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.


Genetic Algorithm Crossover Operator Conjunctive Normal Form Uniform Crossover Adaptive Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Kanta Vekaria
    • 1
  • Chris Clack
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK

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