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Non-Uniform Mapping in Binary-Coded Genetic Algorithms

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 201)

Abstract

Binary-coded genetic algorithms (BGAs) traditionally use a uniform mapping to decode strings to corresponding real-parameter variable values. In this paper, we suggest a non-uniform mapping scheme for creating solutions towards better regions in the search space, dictated by BGA’s population statistics. Both variable-wise and vector-wise non-uniform mapping schemes are suggested. Results on five standard test problems reveal that the proposed non-uniform mapping BGA (or NBGA) is much faster in converging close to the true optimum than the usual uniformly mapped BGA. With the base-line results, an adaptive NBGA approach is then suggested to make the algorithm parameter-free. Results are promising and should encourage further attention to non-uniform mapping strategies with binary coded GAs.

Keywords

  • Non-uniform mapping
  • Binary-coded genetic algorithms
  • Optimization
  • Adaptive algorithm.

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Correspondence to Kalyanmoy Deb .

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© 2013 Springer India

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Deb, K., Dhebar, Y.D., Pavan, N. (2013). Non-Uniform Mapping in Binary-Coded Genetic Algorithms. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_12

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  • DOI: https://doi.org/10.1007/978-81-322-1038-2_12

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1037-5

  • Online ISBN: 978-81-322-1038-2

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