Unpacking and understanding evolutionary algorithms

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13 Citations (Scopus)

Abstract

Theoretical analysis of evolutionary algorithms (EAs) has made significant progresses in the last few years. There is an increased understanding of the computational time complexity of EAs on certain combinatorial optimisation problems. Complementary to the traditional time complexity analysis that focuses exclusively on the problem, e.g., the notion of NP-hardness, computational time complexity analysis of EAs emphasizes the relationship between algorithmic features and problem characteristics. The notion of EA-hardness tries to capture the essence of when and why a problem instance class is hard for what kind of EAs. Such an emphasis is motivated by the practical needs of insight and guidance for choosing different EAs for different problems. This chapter first introduces some basic concepts in analysing EAs. Then the impact of different components of an EA will be studied in depth, including selection, mutation, crossover, parameter setting, and interactions among them. Such theoretical analyses have revealed some interesting results, which might be counter-intuitive at the first sight. Finally, some future research directions of evolutionary computation will be discussed. © 2012 Springer-Verlag.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence : IEEE World Congress on Computational Intelligence, WCCI 2012, Brisbane, Australia, June 10-15, 2012. Plenary/Invited Lectures
EditorsJing LIU, Cesare ALIPPI, Bernadette BOUCHON-MEUNIER, Garrison W. GREENWOOD, Hussein A. ABBASS
PublisherSpringer Berlin Heidelberg
Pages60-76
Number of pages17
ISBN (Electronic)9783642306877
ISBN (Print)9783642306860
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
Volume7311
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Congress

Congress2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/TerritoryAustralia
CityBrisbane
Period10/06/1215/06/12

Keywords

  • IEEE Transaction
  • Evolutionary Algorithm
  • Problem Size
  • Evolutionary Computation
  • Memetic Algorithm

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