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Structure and metaheuristics

机译:结构和元启发法

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摘要

Metaheuristics have often been shown to be effective for difficult combinatorial optimization problems. The reason for that, however, remains unclear. A framework for a theory of metaheuristics crucially depends on a formal representative model of such algorithms. This paper unifies/reconciles in a single framework the model of a black box algorithm coming from the no-free-lunch research (e.g. Wolpert et al. [25], Wegener [23]) with the study of fitness landscape. Both are important to the understanding of meta-heuristics, but they have so far been studied separately. The new model is a natural environment to study meta-heuristics.
机译:元启发法通常已被证明对困难的组合优化问题有效。但是,其原因尚不清楚。元启发式理论的框架至关重要地依赖于此类算法的正式代表性模型。本文统一/协调了来自非自由午餐研究(例如Wolpert等人[25],Wegener [23])和健身景观< / I>。两者对于理解元启发式算法都很重要,但是到目前为止,它们已经分别进行了研究。新模型是研究元启发法的自然环境。

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