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A Modern Introduction to Memetic Algorithms

In: Handbook of Metaheuristics

Author

Listed:
  • Pablo Moscato

    (The University of Newcastle)

  • Carlos Cotta

    (Universidad de Málaga)

Abstract

Memetic algorithms are optimization techniques based on the synergistic combination of ideas taken from different algorithmic solvers, such as population-based search (as in evolutionary techniques) and local search (as in gradient-ascent techniques). After providing some historical notes on the origins of memetic algorithms, this work shows the general structure of these techniques, including some guidelines for their design. Some advanced topics such as multiobjective optimization, self-adaptation, and hybridization with complete techniques (e.g., branch-and-bound) are subsequently addressed. This chapter finishes with an overview of the numerous applications of these techniques and a sketch of the current development trends in this area.

Suggested Citation

  • Pablo Moscato & Carlos Cotta, 2010. "A Modern Introduction to Memetic Algorithms," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 141-183, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_6
    DOI: 10.1007/978-1-4419-1665-5_6
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    Citations

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    Cited by:

    1. Zhang, Zhenzhen & Liu, Mengyang & Lim, Andrew, 2015. "A memetic algorithm for the patient transportation problem," Omega, Elsevier, vol. 54(C), pages 60-71.
    2. Stefan Andreea-Mirabela, 2020. "Metaheuristichybridization: Memeticalgorithm," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 1(48), pages 155-164, August.
    3. Cattaruzza, Diego & Absi, Nabil & Feillet, Dominique & Vidal, Thibaut, 2014. "A memetic algorithm for the Multi Trip Vehicle Routing Problem," European Journal of Operational Research, Elsevier, vol. 236(3), pages 833-848.
    4. Simona Mancini, 2013. "Multi-echelon distribution systems in city logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 54, pages 1-2.
    5. Jorge Daniel Mello-Román & Adolfo Hernández & Julio César Mello-Román, 2021. "Improved Predictive Ability of KPLS Regression with Memetic Algorithms," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    6. Johannes Inführ & Günther Raidl, 2016. "A memetic algorithm for the virtual network mapping problem," Journal of Heuristics, Springer, vol. 22(4), pages 475-505, August.
    7. Diego Cattaruzza & Nabil Absi & Dominique Feillet, 2016. "The Multi-Trip Vehicle Routing Problem with Time Windows and Release Dates," Transportation Science, INFORMS, vol. 50(2), pages 676-693, May.
    8. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.

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