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SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components

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Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

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Abstract

In this paper, a new algorithm named SGEGC was proposed. Inspired by selfish gene theory, SGEGC uses a vector of survival rate to model the condition distribution, which serves as a virtual population that is used to generate new individuals. While the present Estimation of Distribution Algorithms (EDAs) require much time to learn the complex relationships among variables, SGEGC employs an approach that exchanges the relevant genetic components. Experimental results show that the proposed approach is more efficient in convergent reliability and convergent velocity in comparison with BMDA, COMIT and MIMIC in the test functions.

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© 2009 Springer-Verlag Berlin Heidelberg

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Yang, C., Li, Y., Lin, Z. (2009). SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_7

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  • DOI: https://doi.org/10.1007/978-3-642-04843-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04842-5

  • Online ISBN: 978-3-642-04843-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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