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International Conference on Evolutionary Programming

EP 1997: Evolutionary Programming VI pp 297–307Cite as

An analysis of evolutionary algorithms based on neighbourhood and step sizes

  • Theory and Analysis of Evolutionary Computations
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1213))

Abstract

Evolutionary algorithms (EAs) can be regarded as algorithms based on neighbourhood search, where different search operators (such as crossover and mutation) determine different neighbourhood and step sizes. This paper analyses the efficiency of various mutations in evolutionary programming (EP) by examining their neighbourhood and step sizes. It shows analytically when and why Cauchy mutation-based fast EP (FEP) [1, 2] is better than Gaussian mutation-based classical EP (CEP). It also studies the relationship between the optimality of the solution and the time used to find the solution. Based on the theoretical analysis, an improved FEP (IFEP) is proposed, which combines the advantages of both Cauchy and Gaussian mutations in EP. Although IFEP is very simple and requires no extra parameters, it performs better than both FEP and CEP for a number of benchmark problems.

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References

  1. X. Yao and Y. Liu, “Fast evolutionary programming,” in Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming (L. J. Fogel, P. J. Angeline, and T. Bäck, eds.), MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  2. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, 1996. Submitted.

    Google Scholar 

  3. T. Bäck and H.-P. Schwefel, “An overview of evolutionary algorithms for parameter optimization,” Evolutionary Computation, vol. 1, no. 1, pp. 1–23, 1993.

    Google Scholar 

  4. D. K. Gehlhaar and D. B. Fogel, “Tuning evolutionary programming for conformation ally flexible molecular docking,” in Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming (L. J. Fogel, P. J. Angeline, and T. Bäck, eds.), pp. 419–429, MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  5. D. B. Fogel, “An introduction to simulated evolutionary optimisation,” IEEE Trans. on Neural Networks, vol. 5, no. 1, pp. 3–14, 1994.

    Article  Google Scholar 

  6. W. Feller, An Introduction to Probability Theory and Its Applications, vol. 2. John Wiley & Sons, Inc., 2nd ed., 1971.

    Google Scholar 

  7. R. A. Hunt, Calculus with Analytic Geometry. New York, NY 10022-5299: Harper & Row Publ., Inc., 1986.

    Google Scholar 

  8. D. B. Fogel, System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Needham Heights, MA 02194: Ginn Press, 1991.

    Google Scholar 

  9. D. B. Fogel, Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. New York, NY: IEEE Press, 1995.

    Google Scholar 

  10. X. Yao (ed.), “Special issue on evolutionary computation,” Informatica, vol. 18, pp. 375–450, 1994.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Intelligence Group, School of Computer Science, University College, The University of New South Wales Australian Defence Force Academy, 2600, Canberra, ACT, Australia

    Xin Yao, Guangming Lin & Yong Liu

Authors
  1. Xin Yao
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  2. Guangming Lin
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  3. Yong Liu
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Editor information

Peter J. Angeline Robert G. Reynolds John R. McDonnell Russ Eberhart

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© 1997 Springer-Verlag Berlin Heidelberg

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Yao, X., Lin, G., Liu, Y. (1997). An analysis of evolutionary algorithms based on neighbourhood and step sizes. In: Angeline, P.J., Reynolds, R.G., McDonnell, J.R., Eberhart, R. (eds) Evolutionary Programming VI. EP 1997. Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014820

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  • DOI: https://doi.org/10.1007/BFb0014820

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62788-3

  • Online ISBN: 978-3-540-68518-0

  • eBook Packages: Springer Book Archive

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