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The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness...
Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The...
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...
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