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Bio-Inspired Computing - Theories and Applications pp 35–39Cite as

Real-Coded Genetic Algorithm with Oriented Search towards Promising Region for Parameter Optimization

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 472))

Abstract

In this paper, a novel real-coded genetic algorithm is presented to generate offspring towards a promising polygon field with k+1 vertexes, which represents a set of promising points in the entire population at a particular generation. A set of 13 test problems available in the global parameter optimization literature is used to test the performance of the proposed real-coded genetic algorithms. Simulations show the proposed approach is a significant evolutionary computing to efficiently solve parameter optimization problems.

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References

  1. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computations 13(2), 298–417 (2009)

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Chongqing Technology and Business University, 19, Xuefu Avenue, Nan’an, Chongqing, 400067, China

    Zhiqiang Chen, Yun Jiang & Xudong Chen

Authors
  1. Zhiqiang Chen
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  2. Yun Jiang
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  3. Xudong Chen
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Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, 430074, Wuhan, China

    Linqiang Pan

  2. Institute of Mathematics of the Romanian Academy, 014700, Bucuresti, Romania

    Gheorghe Păun

  3. Department of Computer Science and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes s/n., 41012, Sevilla, Spain

    Mario J. Pérez-Jiménez

  4. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Tao Song

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© 2014 Springer-Verlag Berlin Heidelberg

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Cite this paper

Chen, Z., Jiang, Y., Chen, X. (2014). Real-Coded Genetic Algorithm with Oriented Search towards Promising Region for Parameter Optimization. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_6

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  • DOI: https://doi.org/10.1007/978-3-662-45049-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45048-2

  • Online ISBN: 978-3-662-45049-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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