Analysing the Predictivity of Features to Characterise the Search Space

TL;DR

This paper investigates how different features can predict and characterize search spaces to improve problem-solving efficiency, using landscape analysis and machine learning to identify the most effective features.

Contribution

It introduces a method to analyze feature predictivity for better search space characterization, aiding transferability across domains.

Findings

Identified key features that predict search space characteristics

Demonstrated the effectiveness of landscape analysis with machine learning

Enhanced understanding of feature importance for search space modeling

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.