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How Are We Doing? Predicting Evolutionary Algorithm Performance

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

Given an evolutionary algorithm for a problem and an instance of the problem, the results of several trials of the EA on the instance constitute a sample from the distribution of all possible results of the EA on the instance. From this sample, we can estimate, non-parametrically or parametrically, the probability that another run of the EA, independent of the initial ones, will identify a better solution to the instance than any seen in the initial trials. We derive such probability estimates and test the derivations using a genetic algorithm for the traveling salesman problem. We find that while the analysis holds promise, it should probably not depend on the assumption that the distribution of an EA’s results is normal.

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References

  1. Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesmen: The genetic edge recombination operator. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, CA, pp. 133–140. Morgan Kaufmann Publishers, San Francisco (1989)

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  2. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolutionary Programs, 3rd edn. Springer, Berlin (1996)

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  3. Reinelt, G.: TSPLIB - A traveling salesman problem library. ORSA Journal on Computing 3, 376–384 (1991)

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Author information

Authors and Affiliations

  1. Department of Computer Science, St. Cloud State University, St. Cloud, MN, 56301, USA

    Mark A. Renslow, Brenda Hinkemeyer & Bryant A. Julstrom

Authors
  1. Mark A. Renslow
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  2. Brenda Hinkemeyer
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  3. Bryant A. Julstrom
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Renslow, M.A., Hinkemeyer, B., Julstrom, B.A. (2004). How Are We Doing? Predicting Evolutionary Algorithm Performance. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_8

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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