The Novel Approach of Adaptive Twin Probability for Genetic Algorithm

TL;DR

This paper introduces an adaptive twin probability mechanism in genetic algorithms, inspired by natural twin birth, which dynamically adjusts based on fitness to improve accuracy and convergence speed.

Contribution

It proposes a novel adaptive twin probability operator that enhances genetic algorithm performance by dynamically adjusting twin offspring generation based on fitness.

Findings

Increased accuracy in optimization results

Reduced convergence time

Effective on benchmark functions

Abstract

The performance of GA is measured and analyzed in terms of its performance parameters against variations in its genetic operators and associated parameters. Since last four decades huge numbers of researchers have been working on the performance of GA and its enhancement. This earlier research work on analyzing the performance of GA enforces the need to further investigate the exploration and exploitation characteristics and observe its impact on the behavior and overall performance of GA. This paper introduces the novel approach of adaptive twin probability associated with the advanced twin operator that enhances the performance of GA. The design of the advanced twin operator is extrapolated from the twin offspring birth due to single ovulation in natural genetic systems as mentioned in the earlier works. The twin probability of this operator is adaptively varied based on the fitness of best individual thereby relieving the GA user from statically defining its value. This novel approach of adaptive twin probability is experimented and tested on the standard benchmark optimization test functions. The experimental results show the increased accuracy in terms of the best individual and reduced convergence time.