Feature-based search space characterisation for data-driven adaptive operator selection

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

Abstract

Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.

• Journal Article Type: Article
• Acceptance Date: Nov 23, 2023
• Online Publication Date: Dec 22, 2023
• Publication Date: Feb 1, 2024
• Deposit Date: Nov 23, 2023
• Publicly Available Date: Feb 8, 2024
• Journal: Evolving Systems
• Print ISSN: 1868-6478
• Electronic ISSN: 1868-6486
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 1
• Pages: 99-114
• DOI: https://doi.org/10.1007/s12530-023-09560-7
• Keywords: Binary optimisation, Search space characterisation, Supervised machine learning, Artificial bee colonies, Adaptive operator selection
• Public URL: https://uwe-repository.worktribe.com/output/11458820