Plug-and-Play Hyper-heuristics: An Extended Formulation

Patricia Ryser-Welch, J. Miller
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引用次数: 4

Abstract

Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.
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即插即用超启发式:一个扩展公式
超启发式是一个非常活跃的领域,一直在发展。生物智能系统的这一领域涵盖了广泛的算法选择技术。这种类型的自组织机制使用启发式来优化启发式。许多讨论集中在从超启发式中获得的问题解的质量上,很少讨论集中在生成的算法本身。一些超启发式框架往往是高度受限的,它们有限的指令集阻止了最先进的算法的表达。此外,生成的算法通常不是人类可读的。在本文中,我们提出了一些现有的超启发式公式的可能扩展,从而可以解决一些当前开放的问题,并且可以产生自组织启发式,当问题类别发生变化时自动适应环境。再加上对进化算法的分析,可能会产生与人类竞争的结果。
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