Dispatching rules are often the method of choice for solving scheduling problems since they are fast, simple, and adaptive approaches. In recent years genetic programming has increasingly been used to automatically create dispatching rules for various scheduling problems. Since genetic programming is a stochastic approach, it needs to be executed several times to ascertain that good dispatching rules were obtained. This paper analyses whether combining several dispatching rules into an ensemble leads to performance improvements over the individual dispatching rules. Two methods for creating ensembles of dispatching rules, based on the sum and vote methods applied in machine learning, are used and their effectiveness is analysed with regards to the size of the ensemble, the genetic programming method used to generate the dispatching rules, the size of the evolved dispatching rules, and the method used for creating the ensembles. The results demonstrate that the generated ensembles achieve significant improvements over individual automatically generated dispatching rules.

中文翻译：

---

通过简单的整体组合创建调度规则

调度规则通常是解决调度问题的首选方法，因为它们是快速，简单且自适应的方法。近年来，基因编程已越来越多地用于为各种调度问题自动创建调度规则。由于遗传编程是一种随机方法，因此需要执行多次以确保获得了良好的调度规则。本文分析了将多个调度规则组合成一个整体是否会导致单个调度规则的性能提高。基于机器学习中应用的和和投票方法，使用了两种创建调度规则集合的方法，并针对集合的大小，用于生成调度规则的遗传编程方法分析了其有效性，改进的调度规则的大小，以及用于创建集合的方法。结果表明，与单个自动生成的调度规则相比，生成的集合实现了显着的改进。