Analysing the Predictivity of Features to Characterise the Search Space

• URL: http://arxiv.org/abs/2209.06114v1
• Date: Sun, 11 Sep 2022 20:04:17 GMT
• Title: Analysing the Predictivity of Features to Characterise the Search Space
• Authors: Rafet Durgut, Mehmet Emin Aydin, Hisham Ihshaish, Abdur Rakib
• Abstract summary: 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. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.
• Score: 1.5484595752241122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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