Fuzzy Hyperheuristic Framework for GA Parameters Tuning

Recent works using hyper-heuristics for solving clustering problems have been focusing on Genetic Algorithm. However, to the best of this research knowledge, no work is using hyper-heuristics dedicated for tuning the Genetic algorithm`s chromosome size for automatic clustering problem. The ability to tune the chromosome size is important because it allows the automatic clustering algorithm to be adaptive and dynamic. This paper proposes and evaluates a modified Improvement Selection Rules hyper-heuristic algorithm for tuning automatic genetic clustering chromosome size. The paper reviews related works of Genetic algorithm`s parameters tuning and selective hyper-heuristic algorithms and proposes a modified algorithm. The Iris, Breast Cancer, Wine and E-coli datasets are used for evaluation of the algorithm, based on the fitness, accuracy and robustness. The results indicate that the hyper-heuristic algorithm has produced good performance (fitness) and accuracy but consume considerably higher execution times.

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Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization

Zamli

2018

2018 International Conference on Information and Communications Technology (ICOIACT)

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Fuzzy Hyperheuristic Framework for GA Parameters Tuning

Cited by 3 publications

References 14 publications

Prey predator hyperheuristic

Prey predator hyperheuristic

Modified ISR hyper-heuristic for tuning automatic genetic clustering chromosome size

Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization

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