earticle

영어

This work addresses the problem of instance reduction using a distributed implementation of genetic algorithms. Different existing parallel and distributed models for parallelizing genetic algorithms are investigated and applied here to solve the problem of instance reduction. a new parallel model is proposed and implemented we called Global Control Parallel Genetic Algorithm. The results showed enormous reduction in time of 90% over the other models. The resulted dataset showed an acceptable accuracy results on average over all datasets. The model achieved a better reduction in dataset size of 90.22% compared to the other models that didn’t get better that 87.91%. Thus, the proposed distributed system model for instance reduction showed better performance over all model in reducing the time and even reducing the training dataset size while maintaining the same level of accuracy of the original sequential genetic algorithm.