Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network

The most vital step in mining data's in order to have a proper decision making is the classification, it is remains important in multiple of human activities such as the industrial applications, marketing campaigns, research process and the scientific endeavors. The process of classifying involves the objects categorization into classes that are already defined. These categorizations are developed according to the identical attributes of the items or the objects. Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faster convergence speed. The algorithm based on water cycle that includes the evaporation, condensation and precipitation (WC-ECP), which is a population based metaheuristic is used in the paper to improve the accuracy in the feed forward neural network (PNN-probabilistic neural network) to standardizes its random constraint choice and in turn improvise the accuracy of the categorization and the speed of the convergence. The proposed method was tested with the five dataset of UCI machine learning repository and was evinced that the WCECP-PNN performed better compared to the other evolutionary algorithms such as the GA which is also a population based Meta-heuristics