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

In: Handbook of Metaheuristics

Author

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  • Darrell Whitley

    (Colorado State University)

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.

Suggested Citation

  • Darrell Whitley, 2019. "Next Generation Genetic Algorithms: A User’s Guide and Tutorial," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 245-274, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_8
    DOI: 10.1007/978-3-319-91086-4_8
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    Cited by:

    1. He, Dongdong & Ceder, Avishai (Avi) & Zhang, Wenyi & Guan, Wei & Qi, Geqi, 2023. "Optimization of a rural bus service integrated with e-commerce deliveries guided by a new sustainable policy in China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    2. He, Dongdong & Guan, Wei, 2023. "Promoting service quality with incentive contracts in rural bus integrated passenger-freight service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    3. Andrea Ferigo & Giovanni Iacca, 2020. "A GPU-Enabled Compact Genetic Algorithm for Very Large-Scale Optimization Problems," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    4. Silva, Allyson & Aloise, Daniel & Coelho, Leandro C. & Rocha, Caroline, 2021. "Heuristics for the dynamic facility location problem with modular capacities," European Journal of Operational Research, Elsevier, vol. 290(2), pages 435-452.
    5. Wanida Khamprapai & Cheng-Fa Tsai & Paohsi Wang & Chi-En Tsai, 2021. "Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing," Mathematics, MDPI, vol. 9(15), pages 1-17, July.

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