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The performance of GA is measured and analyzed in terms of its performanceparameters against variations in its genetic operators and associatedparameters. Since last four decades huge numbers of researchers have beenworking on the performance of GA and its enhancement. This earlier researchwork on analyzing the performance of GA enforces the need to furtherinvestigate the exploration and exploitation characteristics and observe itsimpact on the behavior and overall performance of GA. This paper introduces thenovel approach of adaptive twin probability associated with the advanced twinoperator that enhances the performance of GA. The design of the advanced twinoperator is extrapolated from the twin offspring birth due to single ovulationin natural genetic systems as mentioned in the earlier works. The twinprobability of this operator is adaptively varied based on the fitness of bestindividual thereby relieving the GA user from statically defining its value.This novel approach of adaptive twin probability is experimented and tested onthe standard benchmark optimization test functions. The experimental resultsshow the increased accuracy in terms of the best individual and reducedconvergence time.

The Novel Approach of Adaptive Twin Probability for Genetic Algorithm