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Modeling, Simulation and Optimization pp 581–598Cite as

An Overview of Crossover Techniques in Genetic Algorithm

Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 206)

Abstract

Artificial Genetic Algorithm is proposed to mimic the natural selection process. It provides an elegant and relatively simple way to solve non-polynomial problems. The crossover, one of the basic step of GA, is an imitation of reproduction in biological beings. Crossover exchanges information between different individuals to generate offspring with the hope of obtaining better genes. The basic aim of crossover is the same, but various approaches are proposed depending on the problem. This paper presents a review on the crossover, basics and some problem-specific techniques.

Keywords

  • GA
  • Genes
  • Chromosomes
  • Crossover
  • Optimization

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, 788010, India

    Joseph L. Pachuau, Arnab Roy & Anish Kumar Saha

Authors
  1. Joseph L. Pachuau
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  2. Arnab Roy
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  3. Anish Kumar Saha
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Corresponding author

Correspondence to Anish Kumar Saha .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Silchar, Assam, India

    Dr. Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Dr. Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Prof. Sivaji Bandyopadhyay

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Dr. Valentina Emilia Balas

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Pachuau, J.L., Roy, A., Kumar Saha, A. (2021). An Overview of Crossover Techniques in Genetic Algorithm. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-15-9829-6_46

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