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A Comparison of Simulated Annealing with a Simple Evolutionary Algorithm

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Foundations of Genetic Algorithms (FOGA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3469))

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Abstract

Evolutionary algorithms belong to the class of general randomized search heuristics. Theoretical investigations often concentrate on simple instances like the well-known (1+1) EA. This EA is very similar to simulated annealing, another general randomized search heuristic. These two algorithms are systematically compared under the perspective of the expected optimization time when optimizing pseudo-boolean functions. It is investigated how well the algorithmic similarities can be exploited to transfer analytical results from one algorithm to the other. Limitations of such an approach are illustrated by the presentation of example functions where the performance of the two algorithms differs in an extreme way. Furthermore, an attempt is made to characterize classes of functions where such a transfer of results is more successful.

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Author information

Authors and Affiliations

  1. FB 4, LS 2, Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen

Authors
  1. Thomas Jansen
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, University of Montana, 59812, Missoula, Montana, USA

    Alden H. Wright

  2. Computer Science Department, University of Tennessee, 37996, Knoxville, TN, USA

    Michael D. Vose

  3. Computer Science Department, George Mason University, 4400 University Drive, 22030, Fairfax, VA, USA

    Kenneth A. De Jong

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, Aizu-Wakamatsu City, Fukushima Prefecture, Japan

    Lothar M. Schmitt

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© 2005 Springer-Verlag Berlin Heidelberg

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Jansen, T. (2005). A Comparison of Simulated Annealing with a Simple Evolutionary Algorithm. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds) Foundations of Genetic Algorithms. FOGA 2005. Lecture Notes in Computer Science, vol 3469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11513575_3

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  • DOI: https://doi.org/10.1007/11513575_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27237-3

  • Online ISBN: 978-3-540-32035-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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