Reduction Technique for Instance-Based Learning Using Distributed Genetic Algorithms
This paper 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 the authors 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%.