TY - GEN

T1 - Group decision making hyper-heuristics for function optimisation

AU - Ozcan, Ender

AU - Misir, Mustafa

AU - Kheiri, Ahmed

PY - 2013/12/31

Y1 - 2013/12/31

N2 - A hyper-heuristic is a high level methodology which performs search over the space of heuristics each operating on the space of solutions to solve hard computational problems. This search process is based on either generation or selection of low level heuristics. The latter approach is used in selection hyper-heuristics. A generic selection hyper-heuristic has two main components which operate successively: heuristic selection and move acceptance methods. An initially generated solution is improved iteratively using these methods. At a given step, the most appropriate heuristic is selected from a fixed set of low level heuristics and applied to a candidate solution producing a new one. Then, a decision is made whether to accept or reject the new solution. This process is repeated until the termination criterion is satisfied. There is strong empirical evidence that the choice of selection hyper-heuristic influences its overall performance. This is one of the first studies to the best of our knowledge that suggests and explores the use of group decision making methods for move acceptance in selection hyper-heuristics. The acceptance decision for a move is performed by multiple methods instead of a single one. The performance of four such group decision making move acceptance methods are analysed within different hyper-heuristics over a set of benchmark functions. The experimental results show that the group decision making strategies have potential to improve the overall performance of selection hyper-heuristics.

AB - A hyper-heuristic is a high level methodology which performs search over the space of heuristics each operating on the space of solutions to solve hard computational problems. This search process is based on either generation or selection of low level heuristics. The latter approach is used in selection hyper-heuristics. A generic selection hyper-heuristic has two main components which operate successively: heuristic selection and move acceptance methods. An initially generated solution is improved iteratively using these methods. At a given step, the most appropriate heuristic is selected from a fixed set of low level heuristics and applied to a candidate solution producing a new one. Then, a decision is made whether to accept or reject the new solution. This process is repeated until the termination criterion is satisfied. There is strong empirical evidence that the choice of selection hyper-heuristic influences its overall performance. This is one of the first studies to the best of our knowledge that suggests and explores the use of group decision making methods for move acceptance in selection hyper-heuristics. The acceptance decision for a move is performed by multiple methods instead of a single one. The performance of four such group decision making move acceptance methods are analysed within different hyper-heuristics over a set of benchmark functions. The experimental results show that the group decision making strategies have potential to improve the overall performance of selection hyper-heuristics.

U2 - 10.1109/UKCI.2013.6651324

DO - 10.1109/UKCI.2013.6651324

M3 - Conference contribution/Paper

AN - SCOPUS:84891078324

SP - 327

EP - 333

BT - 2013 13th UK Workshop on Computational Intelligence, UKCI 2013

PB - IEEE

T2 - 2013 13th UK Workshop on Computational Intelligence, UKCI 2013

Y2 - 9 September 2013 through 11 September 2013

ER -