Browsing by Author "Lang, Ryan Dieter"

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    Landscape analysis-based automated algorithm selection
    (Stellenbosch : Stellenbosch University, 2024-03) Lang, Ryan Dieter; Engelbrecht, Andries; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: The algorithm selection problem, which was developed by Rice, refers to the chal- lenge of choosing the best algorithm available to solve a specific p roblem. The framework put forth by Rice includes four spaces: the problem space, the algorithm space, the characteristic space, and the performance space. In this thesis, the focus is on the problem space of continuous-valued single-objective, boundary-constrained optimisation problems. Landscape analysis, which is a set of techniques that utilises mathematical and statistical methods to characterise the properties of optimisation problems, can be used to describe the characteristic space. Selection of the most effective algorithm to solve optimisation problems is a complex task, because meta- heuristics have varying strengths. Therefore, a data-driven approach that utilises landscape analysis techniques is employed in this thesis to create an automated algorithm selector. This thesis proposes enhancements to the characteristic space by critically evaluat- ing the reliability of the methods used in generating landscape analysis measures. Additionally, a new benchmark suite, which is more representative of the problem space than commonly used benchmark suites in the literature, is proposed. An investigation into the impact on the performance of hybrid metaheuristics created by combining the sampling algorithms used to calculate landscape analysis features with standard metaheuristics, which are used to solve the optimisation problem, is conducted. By combining the proposed improvements to the characteristic, algo- rithm, and problem spaces of the algorithm selection framework, this thesis concludes with a landscape analysis-based automated algorithm selection model that outper- forms the current automated algorithm selection models found in the literature in terms of performance and generalisability. Furthermore, this thesis explores the use of automated machine learning and hybrid metaheuristics in automated algorithm selection.