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A Diversification Operator for Genetic Algorithms

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  • Ghosh, Diptesh

Abstract

Conventional genetic algorithms suffer from a dependence on the initial generation used by the algorithm. In case the generation cosnsists of solutions which are not close enough to a global optimum but some of which are close to a relatively good local optimum, the algorithm is often guided a converge to the local optimum. In this paper, we provide a method which allows a genetic algorithm to search the solution space more effectively, and increases its chance to attain a global optimum. We provide computational experience with real-valued genetic algorithms on functions of two variables.

Suggested Citation

  • Ghosh, Diptesh, 2011. "A Diversification Operator for Genetic Algorithms," IIMA Working Papers WP2011-01-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    • Handle: RePEc:iim:iimawp:9909
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    Cited by:

    1. Ghosh, Diptesh, 2016. "Incorporating gender and age in genetic algorithms to solve the indexing problem," IIMA Working Papers WP2016-03-32, Indian Institute of Management Ahmedabad, Research and Publication Department.

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