On the Applicability of Diploid Genetic Algorithms in Dynamic Environments

Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid counterpart. The work proves the above point by applying Diploid genetic Algorithms to Dynamic Travelling Salesman Problem and comparing the results to Greedy Approach and Simple Genetic Algorithms. The work also presents a hybrid approach namely Greedy Genetic Approach. The results of the experiments proved the fact that diploidity ensures robustness. In the experiments carried out, the three variants of dominance were implemented and 115 trials bought forth the point that though Haploid and Greedy Approaches do not outperform the other, Diploid are the best bet for dynamic environments.