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Unpacking and Understanding Evolutionary Algorithms

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Advances in Computational Intelligence (WCCI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7311))

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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.

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Author information

Authors and Affiliations

  1. CERCIA, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Xin Yao

Authors
  1. Xin Yao
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Editor information

Editors and Affiliations

  1. Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, 710071, Xi’an, Shaanxi, China

    Jing Liu

  2. Dipartimento di Elettronica e Informazione, Politecnico di Milano, 20133, Milano, Italy

    Cesare Alippi

  3. Université Pierre et Marie Curie - Paris 6, LIP6, 4 place Jussieu, 75005, Paris, France

    Bernadette Bouchon-Meunier

  4. Department of Electrical & Computer Engineering, Portland State University, 97207, Portland, OR, USA

    Garrison W. Greenwood

  5. School of Engineering and Information Technology, University of New South Wales, 2600, Canberra, ACT, Australia

    Hussein A. Abbass

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© 2012 Springer-Verlag Berlin Heidelberg

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Yao, X. (2012). Unpacking and Understanding Evolutionary Algorithms. In: Liu, J., Alippi, C., Bouchon-Meunier, B., Greenwood, G.W., Abbass, H.A. (eds) Advances in Computational Intelligence. WCCI 2012. Lecture Notes in Computer Science, vol 7311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30687-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-30687-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30686-0

  • Online ISBN: 978-3-642-30687-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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