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This paper proposes an improvement of genetic algorithm for optimization problems. In this study, the Rao algorithm was applied in crossover and mutation operators instead of traditional crossover and mutation. The algorithm was tested on six benchmark problems and compared with differential evolution (DE), JDE self-adaptive algorithm, and intersection mutation differential evolution (IMDE) algorithm. The computation results illustrated that the proposed algorithm can produce optimal solutions for three of six functions. Comparing to the other three algorithms, the proposed algorithm has provided the best results. The findings prove that the algorithm should be improved in this direction and show that the algorithm produces several solutions obtained by the previously published methods, especially for the continuous step function, the multimodal function and the discontinuous step function.

This paper proposes a hybrid PSO algorithm with Rao algorithms for breast cancer data classification. In this study, the proposed algorithm improved the process of updating the velocity, and the new position of particle was selected from the best particle classification of efficiency. The Rao algorithm was applied to update the velocity and the new position of particle was selected from the best particle classification of efficiency values. The algorithm was evaluated on Wisconsin Breast Cancer Dataset (WBCD) from UCI Machine Learning Repository. The computational results of the proposed algorithm were compared with PSO, J48, SMO, Random Forest, Multilayer perceptron, and Naive Bayes. The results of the comparison revealed that the proposed algorithm showed high performance for classification at 98%, better than PSO, J48, SMO, Random Forest, and Multilayer perceptron, and Naive Bayes.