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Measurements in Fast Evolutionary Programming

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Computational Intelligence and Intelligent Systems (ISICA 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 107))

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Abstract

A number of mutation operators have been developed in evolutionary programming, such as Gaussian mutations, Cauchy mutations, Lévy mutations, and some mixed mutations. Many results have been obtained only on comparisons of performance among different mutations. In stead of mearly measuring the performance, this paper discusses how to examine the behaviors of Gaussian mutations and Cauchy mutations based on nine measurements including five measurements from fitness distributions, one measurement on survival rate, and the other three measurements on mutation step sizes. The relationships among these nine measurements are further explored.

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References

  1. Fogel, D.B.: System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham Heights (1991)

    Google Scholar 

  2. Fogel, D.B.: Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. IEEE Press, New York (1995)

    MATH  Google Scholar 

  3. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)

    Article  Google Scholar 

  4. Lee, C.Y., Yao, X.: Evolutionary programming using the mutations based on the Lévy probability distribution. IEEE Transactions on Evolutionary Computation 8(1), 1–13 (2004)

    Article  Google Scholar 

  5. Wang, H., Zeng, S., Liu, Y., Wang, W., Shi, H.: Rediversification based particle swarm algorithm with Cauchy mutation. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 362–371. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Li, C., Liu, Y., Zhou, A., Kang, L., Wang, H.: A fast particle swarm optimization algorithm with Cauchy mutation and Natural Selection Strategy. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 334–343. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Wang, H., Liu, Y., Li, C., Zeng, S.: A hybrid particle swarm algorithm with Cauchy mutation. In: Proceedings of 2007 IEEE Swarm Intelligence Symposium, pp. 356–361. IEEE Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  8. Liu, Y., Yao, X.: How to control search step size in fast evolutionary programming. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 652–656. IEEE Press, Los Alamitos (2002)

    Google Scholar 

  9. Yao, X., Lin, G., Liu, Y.: An analysis of evolutionary algorithms based on neighbourhood and step sizes. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 297–307. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  10. Törn, A., Žilinskas, A. (eds.): Global Optimization. LNCS, vol. 350. Springer, Heidelberg (1989)

    MATH  Google Scholar 

  11. Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1(1), 1–23 (1993)

    Article  Google Scholar 

  12. Feller, W.: An Introduction to Probability Theory and Its Applications, 2nd edn., vol. 2. John Wiley & Sons, Inc, Chichester (1971)

    MATH  Google Scholar 

  13. Liu, Y.: Correlation between mutations and self-adaptation in evolutionary programming. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds.) ISICA 2008. LNCS, vol. 5370, pp. 58–66. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  14. Schwefel, H.-P.: Evolution and Optimum Seeking. John Wiley & Sons, New York (1995)

    MATH  Google Scholar 

  15. Liu, Y.: Operator adaptation in evolutionary programming. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 90–99. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Liu, Y. (2010). Measurements in Fast Evolutionary Programming. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_9

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  • DOI: https://doi.org/10.1007/978-3-642-16388-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16387-6

  • Online ISBN: 978-3-642-16388-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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