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On the applicability of diploid genetic algorithms

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Abstract

The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. It is also important to analyse the applicability of the premise of the dominance techniques applied so far in order to implement the technique. The work presents a systematic review of diploid genetic algorithms, examines the premise of the dominance relation used in different works and discusses the future scope. The work also discusses the biological basis of evaluating dominance. The work is important as the future of machine learning relies on techniques that are robust as well as efficient.

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Authors and Affiliations

  1. Department of Computer Science, Jamia Hamdard University, New Delhi, India

    Harsh Bhasin

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, India

    Sushant Mehta

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  1. Harsh Bhasin
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  2. Sushant Mehta
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Correspondence to Harsh Bhasin.

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Bhasin, H., Mehta, S. On the applicability of diploid genetic algorithms. AI & Soc 31, 265–274 (2016). https://doi.org/10.1007/s00146-015-0591-x

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