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A high performance genetic algorithm using bacterial conjugation operator (HPGA)

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Abstract

In this paper an efficient evolutionary algorithm is proposed which could be applied to real-time problems such as robotics applications. The only parameter of the proposed algorithm is the “Population Size” which makes the proposed algorithm similar to parameter-less algorithms, and the only operator applied during the algorithm execution is the bacterial conjugation operator, which makes using and implementation of the proposed algorithm much easier. The procedure of the bacterial conjugation operator used in this algorithm is different from operators of the same name previously used in other evolutionary algorithms such as the pseudo bacterial genetic algorithm or the microbial genetic algorithm. For a collection of 23 benchmark functions and some other well-known optimization problems, the experimental results show that the proposed algorithm has better performance when compared to particle swarm optimization and a simple genetic algorithm.

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Acknowledgments

The authors would like to thank the anonymous referees for their helpful comments and suggestions to improve the paper. Also the authors would like to thank their partners in the University of Tabriz for their cooperation in preparing this paper.

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Authors and Affiliations

  1. School of Engineering Emerging Technologies, University of Tabriz, Tabriz, Iran

    Amir Mehrafsa & Alireza Sokhandan

  2. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Ghader Karimian

Authors
  1. Amir Mehrafsa
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  2. Alireza Sokhandan
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  3. Ghader Karimian
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Correspondence to Ghader Karimian.

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Mehrafsa, A., Sokhandan, A. & Karimian, G. A high performance genetic algorithm using bacterial conjugation operator (HPGA). Genet Program Evolvable Mach 14, 395–427 (2013). https://doi.org/10.1007/s10710-013-9185-x

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  • DOI: https://doi.org/10.1007/s10710-013-9185-x

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