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Fitness tracking based evolutionary programming: a novel approach for function optimization

Published: 06 July 2013 Publication History

Abstract

In order to achieve a satisfactory optimization performance by evolutionary programming (EP), it is necessary to ensure proper balance between exploration and exploitation. It is obvious that one single mutation operator is not the answer. Moreover, early loss of genetic diversity causes premature trapping around locally optimal points of the fitness landscape. This paper presents a fitness tracking based evolutionary programming (FTEP) algorithm incorporating a fitness tracking scheme to find the locally trapped individuals and treat them in a different way so that they are able to improve their performance. FTEP also incorporates several mutation operators in one algorithm and employs a self-adaptive strategy to gradually self-adapt the mutation operators in order to apply an appropriate mutation operator on the individual based on its need. A test-suite of 25 functions has been used to evaluate the performance of FTEP.

References

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X. Yao, Y. Liu and G. Lin. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2):82--102, July 1999.
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T. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Comput., 1(1):1--23, 1993.
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C. Lee and X. Yao. Evolutionary programming using mutations based on the Lévy probability distribution. IEEE Transactions on Evolutionary Comput., 8(1):1--13, February 2004.
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A. K. Qin, V. L. Huang, and P. N. Suganthan. Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput., 13(2):398--417, April 2009.
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P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, and S. Tiwari. Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization{online}. Available: http://www.ntu.edu.sg/home/EPNSugan
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K. Chellapilla. Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation, 2(3):91--96, September 1998.

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Published In

cover image ACM Conferences
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
July 2013
1798 pages
ISBN:9781450319645
DOI:10.1145/2464576
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. evolutionary programming
  2. fitness tracking
  3. mutation
  4. stagnant population

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GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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