论文标题
分析特征的预测性以表征搜索空间
Analysing the Predictivity of Features to Characterise the Search Space
论文作者
论文摘要
探索搜索空间是几十年来吸引研究人员兴趣的最不可预测的挑战之一。处理不可预测性的一种方法是表征搜索空间并采取相应的行动。特征良好的搜索空间可以帮助将问题状态映射到一组运算符，以生成新的问题状态。在本文中，已经使用最知名的机器学习方法分析了基于景观分析的特征集，以确定最佳功能集。但是，为了处理问题的复杂性并引起共同点以跨领域转移经验，最具代表性特征的选择仍然至关重要。提出的方法分析了一组特征的预测性，以确定最佳分类。
Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.
