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A naive genetic approach for non-stationary constrained problems

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Abstract

An algorithm to be effective for solving non-stationary problems should be robust, adaptive to the changing environment and efficient. Genetic algorithms (GAs) are increasingly being used to solve non-stationary problems. We use GA with a new approach of gene induction (Bhatia and Basu in Soft Comput 8(1):1–9, 2003) to solve non-stationary constrained problems. The approach combines high value genes to form chromosomes from the initial population itself. The efficacy of the method is demonstrated on non-stationary versions of 0/1 knapsack and pure-integer programming problems. The results obtained with the approach are compared with those obtained with feedback thermodynamical genetic algorithm (FTDGA) (Mori et al. in 5th parallel problem solving from nature, number 1498 in LNCS, pp 149–157, 1998). It shows that gene-induction approach is more accurate and requires less time compared to the FTDGA.

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Author notes

  1. A. K. Bhatia

    Present address: National Bureau of Animal Genetic Resources, Karnal, 132001, India

Authors and Affiliations

  1. Department of Computer Science, Banaras Hindu University, Varanasi, 221005, India

    S. K. Basu & A. K. Bhatia

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  1. S. K. Basu
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  2. A. K. Bhatia
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Correspondence to S. K. Basu.

Additional information

A preliminary version of the paper appeared in the proceedings of the 5th international conference on advances in pattern recognition (Basu and Bhatia 2003).

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Basu, S.K., Bhatia, A.K. A naive genetic approach for non-stationary constrained problems. Soft Comput 10, 152–162 (2006). https://doi.org/10.1007/s00500-004-0438-8

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