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Next Generation Genetic Algorithms: A User’s Guide and Tutorial

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 272))

Abstract

Genetic algorithms are different from most other metaheuristics because they exploit three key ideas: (1) the use of a population of solutions to guide search, (2) the use of crossover operators that recombine two or more solutions to generate new and potentially better solutions, and (3) the active management of diversity to sustain exploration. New ideas that are also introduced in this chapter include (1) the use of deterministic recombination operators that are capable of tunneling between local optima, and (2) the use of deterministic constant time move operators.

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Authors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Darrell Whitley

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  1. Darrell Whitley
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Correspondence to Darrell Whitley .

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  1. Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montreal, QC, Canada

    Michel Gendreau

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal, Montreal, QC, Canada

    Jean-Yves Potvin

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Whitley, D. (2019). Next Generation Genetic Algorithms: A User’s Guide and Tutorial. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-319-91086-4_8

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