Data Mining based Hybridization of Meta-RaPS | Zendy

Fatemah Al-Duoli | Zendy; Ghaith Rabadi | Zendy

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Data Mining based Hybridization of Meta-RaPS

Author(s) -

Fatemah Al-Duoli,

Ghaith Rabadi

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.09.097

Subject(s) - computer science , metaheuristic , data mining , benchmark (surveying) , process (computing) , decision tree , heuristic , machine learning , tree (set theory) , artificial intelligence , algorithm , mathematical analysis , mathematics , geodesy , geography , operating system

Though metaheuristics have been frequently employed to improve the performance of data mining algorithms, the opposite is not true. This paper discusses the process of employing a data mining algorithm to improve the performance of a metaheuristic algorithm. The targeted algorithms to be hybridized are the Meta-heuristic for Randomized Priority Search (Meta-RaPS) and an algorithm used to create an Inductive Decision Tree. This hybridization focuses on using a decision tree to perform on-line tuning of the parameters in Meta-RaPS. The process makes use of the information collected during the iterative construction and improvement phases Meta-RaPS performs. The data mining algorithm is used to find a favorable parameter using the knowledge gained from previous Meta-RaPS iterations. This knowledge is then used in future Meta-RaPS iterations. The proposed concept is applied to benchmark instances of the Vehicle Routing Problem

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