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Research and Practice of Genetic Algorithm Theory

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Frontier Computing (FC 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 422))

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Abstract

Genetic Algorithm is a class of high collateral, stochastic self-reliance search algorithms which based on mechanism of nature select and nature genetic. The paper introduces the principles of genetic algorithm and its methodology. The algorithm is practiced on the solution to find the maximum value of function in a given interval and the result is satisfied.

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References

  1. Mitchell, Melanie (1996). An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press. ISBN 9780585030944.

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  2. Bridges, Clayton L.; Goldberg, David E. (1987). An analysis of reproduction and crossover in a binary-coded genetic algorithm. 2nd Int’l Conf. on Genetic Algorithms and their applications.

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  3. Akbari, Ziarati (2010). “A multilevel evolutionary algorithm for optimizing numerical functions” IJIEC 2 (2011): 419–430 [1].

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Acknowledgements

This work was supported in part by the Canada NSERC Business Intelligence Network and by the University of Waterloo, in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014601, in part by the National Science Foundation of China under Grants 61300028, in part by the Project of the Ministry of Public Security under Grant 2014JSYJB009.

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Authors and Affiliations

  1. The Third Research Institute of Ministry of Public Security, Shanghai, 201204, People’s Republic of China

    Junyi Gu, Zhiping Wu & Xin Wang

Authors
  1. Junyi Gu
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  2. Zhiping Wu
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  3. Xin Wang
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Corresponding author

Correspondence to Zhiping Wu .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Neil Y. Yen

  2. Department of Information Technology, Overseas Chinese University, Taichung, Taiwan

    Jason C Hung

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© 2018 Springer Nature Singapore Pte Ltd.

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Gu, J., Wu, Z., Wang, X. (2018). Research and Practice of Genetic Algorithm Theory. In: Yen, N., Hung, J. (eds) Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-10-3187-8_11

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  • DOI: https://doi.org/10.1007/978-981-10-3187-8_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3186-1

  • Online ISBN: 978-981-10-3187-8

  • eBook Packages: EngineeringEngineering (R0)

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