Evolutionary Optimization

  • Christian Blum
  • Raymond Chiong
  • Maurice Clerc
  • Kenneth De Jong
  • Zbigniew Michalewicz
  • Ferrante Neri
  • Thomas Weise

Abstract

The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms unclear. Originally, it was coined to put a group of stochastic search algorithms that mimic natural evolution together. While some people would still see it as a specific term devoted to this group of algorithms, including Genetic Algorithms, Genetic Programming, Evolution Strategies, Evolutionary Programming, and to a lesser extent Differential Evolution and Estimation of Distribution Algorithms, many others would regard “Evolutionary Algorithms” as a general term describing population-based search methods that involve some form of randomness and selection. In this chapter, we re-visit the fundamental question of “what is an Evolutionary Algorithm?” not only from the traditional viewpoint but also the wider, more modern perspectives relating it to other areas of Evolutionary Computation. To do so, apart from discussing the main characteristics of this family of algorithms we also look at Memetic Algorithms and the Swarm Intelligence algorithms. From our discussion, we see that establishing semantic borders between these algorithm families is not always easy, nor necessarily useful. It is anticipated that they will further converge as the research from these areas cross-fertilizes each other.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Blum
    • 1
  • Raymond Chiong
    • 2
  • Maurice Clerc
    • 3
  • Kenneth De Jong
    • 4
  • Zbigniew Michalewicz
    • 5
    • 6
    • 7
  • Ferrante Neri
    • 8
  • Thomas Weise
    • 9
  1. 1.ALBCOM Research GroupUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyAustralia
  3. 3.Independent ConsultantGroisyFrance
  4. 4.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA
  5. 5.School of Computer ScienceUniversity of AdelaideAustralia
  6. 6.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  7. 7.Polish-Japanese Institute of Information TechnologyWarsawPoland
  8. 8.Department of Mathematical Information TechnologyUniversity of JyväskyläAgoraFinland
  9. 9.Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and TechnologyUniversity of Science and Technology of China (USTC)ĀnhuīChina

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