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An improved structure of genetic algorithms for global optimisation

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Abstract

Genetic Algorithm (GA) is one of the most general global optimisation solution methods used in countless number of works. However, like other search techniques, GA has weak theoretical guarantee of global optimal solution and can only offer a probabilistic guarantee. Having a GA capable of searching for the global optimal solution with very high success probability is always desirable. In this paper, an innovative structure of GA, in which adaptive restarting and chromosome elite transferring strategies are harmoniously integrated together, is proposed to improve the success rate of achieving global optimal solution of the algorithm. The robustness of the proposed GA structure is demonstrated through a number of case studies.

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Acknowledgments

The first author is grateful to Australian Government for sponsoring his Ph.D. study at the University of South Australia, Australia, in the form of the Endeavour Award.

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Authors and Affiliations

  1. School of Engineering, University of South Australia, Mawson Lakes Campus, Mawson Lakes, SA, 5095, Australia

    Son Duy Dao, Kazem Abhary & Romeo Marian

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  1. Son Duy Dao
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  2. Kazem Abhary
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  3. Romeo Marian
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Correspondence to Son Duy Dao.

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Dao, S.D., Abhary, K. & Marian, R. An improved structure of genetic algorithms for global optimisation. Prog Artif Intell 5, 155–163 (2016). https://doi.org/10.1007/s13748-016-0091-3

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  • DOI: https://doi.org/10.1007/s13748-016-0091-3

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