IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-07124-4_29.html
   My bibliography  Save this book chapter

Memetic Algorithms

In: Handbook of Heuristics

Author

Listed:
  • Carlos Cotta

    (Universidad de Málaga, Departamento Lenguajes y Ciencias de la Computación)

  • Luke Mathieson

    (University of Newcastle, Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine)

  • Pablo Moscato

    (The University of Newcastle, School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment)

Abstract

Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.

Suggested Citation

  • Carlos Cotta & Luke Mathieson & Pablo Moscato, 2018. "Memetic Algorithms," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G. C. Resende (ed.), Handbook of Heuristics, chapter 20, pages 607-638, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-07124-4_29
    DOI: 10.1007/978-3-319-07124-4_29
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-319-07124-4_29. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.