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Genetic Algorithms: A Mature Bio-inspired Optimization Technique for Difficult Problems

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Nature-Inspired Methods for Metaheuristics Optimization

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 16))

Abstract

This chapter is dedicated to the method of genetic algorithms. Aiming at offering the essentials and at encouraging prospective users, it includes: (a) Description of the basic idea and the respective terminology, (b) Presentation of the basic genetic operators (selection, crossover, mutation), together with some additional ones, (c) Investigation of the values of basic parameters (crossover and mutation probability), (d) Outline of techniques for handling constraints and of conditions for the termination of the optimization process, and (e) Discussion on advantages and disadvantages of genetic algorithms. Moreover, the relationship between overall accuracy and optimization process accuracy is discussed, and some hints regarding teaching course modules on genetic algorithms are presented.

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Authors and Affiliations

  1. Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Konstantinos L. Katsifarakis & Yiannis N. Kontos

Authors
  1. Konstantinos L. Katsifarakis
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  2. Yiannis N. Kontos
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Correspondence to Konstantinos L. Katsifarakis .

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Editors and Affiliations

  1. Ecole Central de Nantes, Nantes cedex 3, France

    Fouad Bennis

  2. Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajib Kumar Bhattacharjya

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Katsifarakis, K.L., Kontos, Y.N. (2020). Genetic Algorithms: A Mature Bio-inspired Optimization Technique for Difficult Problems. In: Bennis, F., Bhattacharjya, R. (eds) Nature-Inspired Methods for Metaheuristics Optimization. Modeling and Optimization in Science and Technologies, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-26458-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-26458-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26457-4

  • Online ISBN: 978-3-030-26458-1

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