Adaptive Clone Selection Algorithm for Function Optimization

Wen Xue Yang; Zhe Chen; Cheng Jun Li

doi:10.4028/www.scientific.net/AMM.644-650.2147

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Adaptive Clone Selection Algorithm for Function...

Adaptive Clone Selection Algorithm for Function Optimization

Abstract:

In this paper, we present a scheme to improve immune cloning selection algorithm. The improved algorithm, which is referred to as adaptive cloning selection algorithm (ACSA), is proposed and then applied to function optimization. At first, we present adaptive gene mutation which decides mutation probability of each code point (locus) based on the quality of antibodies and the number of evolution iterations. Secondly, we present an iteratively increasing method from one locus to the all ones, which can be used in function optimization. Then, the cloning selection process of evolution is divided into two-stages. The first step is to increase locus of antibodies. In the other one, the Baldwin effect learning operator is employed. And finally, an experiment is carried out to verify the theoretical analyses on several testing functions.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2147-2150

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2147

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Online since:

September 2014

Authors:

Wen Xue Yang*, Zhe Chen, Cheng Jun Li

Keywords:

Adaptive Gene Mutation, Baldwin Effect, Function Optimization, Increasing from One Locus to the all Ones, Two-Stage Cloning Selection

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* - Corresponding Author

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