Data mining: concepts and techniques
Data mining: concepts and techniques
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment
INFORMS Journal on Computing
Adaptive and Resource-Aware Mining of Frequent Sets
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
GRASP with path-relinking for the maximum diversity problem
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Computational aspects of the maximum diversity problem
Operations Research Letters
Using datamining techniques to help metaheuristics: a short survey
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Heuristics and metaheuristics for the maximum diversity problem
Journal of Heuristics
A hybrid data mining GRASP with path-relinking
Computers and Operations Research
Adaptive and multi-mining versions of the DM-GRASP hybrid metaheuristic
Journal of Heuristics
Hi-index | 0.00 |
The maximum diversity problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among themselves. The identification of such solution is an NP-hard problem. In this work, we propose a hybrid GRASP metaheuristic for the MDP that incorporates a data mining process. Data mining refers to the extraction of new and potentially useful knowledge from datasets in terms of patterns and rules. We believe that data mining techniques can be used to extract patterns that represent characteristics of sub-optimal solutions of a combinatorial optimization problem. Therefore these patterns can be used to guide the search for better solutions in metaheuristics procedures. Performance comparison between related work and the proposed hybrid heuristics is provided. Experimental results show that the new hybrid GRASP is quite robust and, mainly, this strategy is able to find high-quality solutions in less computational time.