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.
翻译:探索搜索空间是数十年来吸引研究人员兴趣的最不可预测的挑战之一。处理不可预测性的方法之一是描述搜索空间的特点并据此采取行动。一个特征清晰的搜索空间可以帮助将问题状态映射成一组操作员,以产生新的问题状态。在本文中,利用最知名的机器学习方法分析了一套基于景观分析的特征,以确定最佳特征集。然而,为了处理问题的复杂性,并促成在跨领域转让经验的共性,选择最具代表性的特征仍然至关重要。拟议方法分析了一套特征的预测性,以确定最佳分类。