In operation research and computer science, a genetic algorithm (GA) is a most powerful meta-heuristic approach, its inspired by the process of natural selection. This approach is usually applied to generate superior quality results to standard and real life functions. Several number of researcher has been solved most number of real applications related to different fields with the help of this technique. After Inspired of these researchers, has been also solved the Breast Cancer and Iris data set problems in this article using some recent metaheuristics of nature inspired. For verification, the solutions are compared with some of the most well-known evolutionary trainers: Particle Genetic Algorithm (GA), Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), Personal Best Position Particle Swarm Optimization (PBPPSO), Evolutionary Strategy (ES), Biogeographical Based Optimization (BBO) and Population based Incremental Learning (PBIL). The numerical and statistical solutions show that GA algorithm is able to provide very competitive solutions in terms of improved local optima avoidance. The solutions also reveal a high level of accuracy in classification.
Narinder Singh, Singh SB and Sharandeep Singh
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