Mining Markov Network Surrogates for Value-Added Optimisation
Mining Markov Network Surrogates for Value-Added Optimisation
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DOI：
10.1145/2908961.2931711
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
A. Brownlee
中科院分区：
文献类型：
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作者：
A. Brownlee
替代健身功能是一种流行的技术，可以通过廉价型号的呼叫来代替昂贵的健身功能的呼叫。但是，替代物还代表了健身函数的明确模型，可以在近似解决方案健身之外利用该模型。本文提出，采矿替代健身模型可以为决策者提供有关问题的有用的其他信息，从而为优化过程增加了价值。介绍了基于马尔可夫网络的现有健身模型，并应用于建筑物外观上玻璃的优化。该模型的分析揭示了仅在优化的一部分运行后，其参数如何指向问题的全局最优值，并揭示了有用的属性，例如问题变量的相对灵敏度。
Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls to a costly fitness function with calls to a cheap model. However, surrogates also represent an explicit model of the fitness function, which can be exploited beyond approximating solution fitness. This paper proposes that mining surrogate fitness models can yield useful additional information on the problem to the decision maker, adding value to the optimisation process. An existing fitness model based on Markov networks is presented and applied to the optimisation of glazing on a building facade. Analysis of the model reveals how its parameters point towards the global optima of the problem after only part of the optimisation run, and reveals useful properties like the relative sensitivities of the problem variables.