A Genetics-Based Machine Learning Approach for Realtime Scheduling
A Genetics-Based Machine Learning Approach for Realtime Scheduling
复制标题
基于遗传学的实时调度机器学习方法
DOI：
10.1541/ieejeiss.123.823
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
K. Matsuda
中科院分区：
文献类型：
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作者：
K. Sakakibara;H. Tamaki;H. Murao;S. Kitamura;T. Iwatani;K. Matsuda
在本文中，我们采用了基于遗传的机器学习（GBML）的方法来实时调度问题，其中几个产品被分配到一个缓冲区，并提出了一种方法来生成和选择规则分配每个产品到一个理想的缓冲区。在应用GBML，我们使用皮特的方法，其中的一组规则（规则集）表示为遗传算法的个人，和个人的适应度计算的基础上所需的运输，经营和保持整个产品的总成本。通过一些计算实验，我们的方法的有效性和可能性进行了研究。
In this paper, we adopt a genetic-based machine learning (GBML) approach to a realtime scheduling problem in which several products are to be assigned to one of the buffers, and propose a method of generating and selecting rules for assigning each product to a desirable buffer. In applying the GBML, we use the Pitt approach, where the set of rules (rule-set) is represented symbolically as an individual of genetic algorithms, and the fitness of an individual is calculated based on the total cost required for transporting, operating and keeping of whole products. Through some computational experiments, the effectiveness and the possibility of our approach is investigated.