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A New Organizational Nonlinear Genetic Algorithm for Numerical Optimization

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Based on the concept of organization in economics, a novel genetic algorithm, organizational nonlinear genetic algorithm (ONGA), is proposed to solve global numerical optimization problems with continuous variables. In ONGA, genetic operators do not act on individuals directly, but on organizations, and four genetic operators,organization establish, organization classify, multi-parent crossover, and multi-parent mutation operators, are designed for organizations. Simulation results indicate that ONGA performs much better than the real-coded genetic algorithm both in the quality of solution and in the computational complexity.

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References

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Author information

Authors and Affiliations

  1. Division of system simulation and computer application, Taiyuan University of Science and Technology, Shanxi, 030024, P.R. China

    Zhihua Cui & Jianchao Zeng

Authors
  1. Zhihua Cui
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  2. Jianchao Zeng
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Cui, Z., Zeng, J. (2005). A New Organizational Nonlinear Genetic Algorithm for Numerical Optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_30

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  • DOI: https://doi.org/10.1007/11539902_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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