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A gradient oriented recombination scheme for evolution strategies

Published: 08 March 2009 Publication History

Abstract

This paper proposes a novel recombination scheme for evolutionary algorithms, which can guide the new population generation towards the maximum increase of the objective function. Given the current sample points and their function evaluations, the Shepard's interpolation method is used to approximate the underlying objective function in that local region. We then compute the gradient of the estimated function which in consequence leads to an iterative process, called the mean shift, for searching the local function optimum. In each mean shift step, we calculate the weighted mean of sample points in the kernel window, followed by shifting the location of the kernel to the computed mean. Such iterative process eventually converges to the point at which the estimated objective function has zero gradient. We use the converged point as the output of our recombination operator. Experimental results show that such gradient based recombination scheme can improve the efficiency of optimization search in evolutionary algorithms.

References

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D. V. Arnold and D. MacDonald. Weighted multirecombination evolution strategies on the parabolic ridge. IEEE Congress on Evolutionary Computation, pages 104--111, 2006.
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T. Bäck. Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford, UK, 1996.
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D. Shepard. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 23rd ACM national conference, pages 517--524, New York, NY, USA, 1968.
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cover image ACM Conferences
SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing
March 2009
2347 pages
ISBN:9781605581668
DOI:10.1145/1529282
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 08 March 2009

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Author Tags

  1. evolutionary computation
  2. gradient estimation
  3. mean shift
  4. recombination

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SAC09
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SAC09: The 2009 ACM Symposium on Applied Computing
March 8, 2009 - March 12, 2008
Hawaii, Honolulu

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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