将神经网络应用于模因算法的协同进化,求解并行调度中的最大完工时间最小化问题

Tatiane Regina Bonfim, A. Yamakami
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引用次数: 2

摘要

这里讨论的问题是在相同的并行机器中调度任务的问题之一。在这个问题中,我们处理一组n个任务和m个相同的并行机器,目标是最小化最大完工时间。makespan是最繁忙机器的总处理时间。本文提出一种模因神经调度器的实现,以解决这个调度问题。模因算法是遗传算法与局部搜索算法的混合版本,用来进化出良好的调度形式;神经网络被用来计算种群中每个个体的适应度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural network applied to the coevolution of the memetic algorithm for solving the makespan minimization problem in parallel machine scheduling
The problem discussed here is one of scheduling the tasks in identical parallel machines. In this problem, we deal with a set of n tasks and m identical parallel machines, with the objective of minimizing the makespan. The makespan is the total processing time of the most busy machine. This work presents an implementation of a memetic-neuro scheduler for solving this scheduling problem. The memetic algorithm, which is an hybrid version of genetic algorithm with local search, has been used to evolve good scheduling forms; and the neural network has been used to calculate the fitness for each individual of the population.
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