Hyper-heuristics for grouping problems
Elhag, Anas (2015) Hyper-heuristics for grouping problems. PhD thesis, University of Nottingham.
Grouping problems are hard to solve combinatorial optimization problems which require partitioning of objects into a minimum number of subsets while another additional objective is simultaneously optimized. Considerable research e ort has recently been directed towards automated problem-independent reusable heuristic search methodologies such as hyper-heuristics, which operate on a space formed by a set of low level heuristics rather than solutions, directly. Hyper-heuristics are commonly split into two main categories: selection hyper-heuristics, which are the focus of the work presented in this thesis, and generation hyper-heuristics. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. At each step, a new solution is produced after a selected heuristic is applied to the solution at hand and then the move acceptance method is used to decide whether the resultant solution replaces the current one or not.
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