Document Type

Article

Publication Date

2014

DOI

10.1016/j.procs.2014.09.097

Publication Title

Procedia Computer Science

Volume

36

Pages

301-307

Abstract

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. 2014 The Authors.

Comments

This is an open access article under the CC BY-NC-ND license:

http://creativecommons.org/licenses/by-nc-nd/3.0/

Original Publication Citation

Al-Duoli, F., & Rabadi, G. (2014). Data mining based hybridization of meta-RaPS. Procedia Computer Science, 36, 301-307. doi:10.1016/j.procs.2014.09.097

ORCID

0000-0001-8145-313X (Rabadi)