Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. Genetic Algorithms
 3. Parallel Genetic Algorithms
 4. Parallel Genetic Algorithm Models
 5. Parallelizing Instance Reduction
 6. The Experiments and Performance Measures
 References

키워드

저자

• Tahseen A. Al-Ramadin [ Al-Hussein Bin Talal University, Ma’an 71111, Jordan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605