A Classification of Hyper-heuristic Approaches
A Classification of Hyper-heuristic Approaches
复制标题
DOI：
10.1007/978-1-4419-1665-5_15
复制
发表时间：
2010-01-01
期刊：
影响因子：
--
通讯作者：
Woodward, John R.
中科院分区：
文献类型：
--
作者：
Burke, Edmund K.;Hyde, Matthew;Woodward, John R.
超启发式研究的当前最新技术包括一组方法，这些方法的共同目标是自动设计和调整启发式方法来解决困难的计算搜索问题。主要目标是产生更普遍适用的搜索方法。在本章中，我们概述了之前的超启发式分类，并提供了统一的分类和定义，其中涵盖了该领域正在开展的工作。我们区分两个主要的超启发式类别：启发式选择和启发式生成。详细讨论了每个类别的一些代表性示例。我们的目标是阐明现有技术的主要特征，并为超启发式研究提出新方向。
The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.