Analysing the predictivity of features to characterise the search space

Durgut, Rafet; Aydin, Mehmet Emin; Ihshaish, Hisham; Rakib, Abdur

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK
• Start Date: Sep 6, 2022
• End Date: Sep 9, 2022
• Acceptance Date: Jul 2, 2022
• Online Publication Date: Sep 7, 2022
• Publication Date: 2022
• Deposit Date: Sep 6, 2022
• Publicly Available Date: Sep 8, 2024
• Publisher: Springer Verlag
• Volume: 13532 LNCS
• Pages: 1-13
• Series Title: Lecture Notes in Computer Science (LNCS, volume 13532)
• Series Number: 13532
• Edition: Proceedings; Part IV
• Book Title: Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV
• Chapter Number: 1
• ISBN: 9783031159367
• DOI: https://doi.org/10.1007/978-3-031-15937-4_1
• Keywords: Feature analysis, Search space characterisation, Supervised machine learning
• Public URL: https://uwe-repository.worktribe.com/output/9952793
• Publisher URL: https://link.springer.com/book/10.1007/978-3-031-15937-4
• Related Public URLs: https://link.springer.com/conference/icann
• Additional Information: First Online: 7 September 2022; Conference Acronym: ICANN; Conference Name: International Conference on Artificial Neural Networks; Conference City: Bristol; Conference Country: United Kingdom; Conference Year: 2022; Conference Start Date: 6 September 2022; Conference End Date: 9 September 2022; Conference Number: 31; Conference ID: icann2022; Conference URL: https://e-nns.org/icann2022/; Type: Single-blind; Conference Management System: EasyChair; Number of Submissions Sent for Review: 561; Number of Full Papers Accepted: 255; Number of Short Papers Accepted: 4; Acceptance Rate of Full Papers: 45% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.; Average Number of Reviews per Paper: 3; External Reviewers Involved: No