Document Type: Research Paper


Department of Electrical Engineering, Faculty of Engineering, Birjand University, Birjand, Iran


The concept of intelligently controlling the search process of gravitational
search algorithm (GSA) is introduced to develop a novel data mining
technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At
first a fuzzy controller is designed for adaptively controlling the gravitational
coefficient and the number of effective objects, as two important parameters
which play major roles on search process of GSA. Then the improved GSA
(namely Fuzzy-GSA) is employed to construct a novel data mining algorithm
for classification rule discovery from reference data sets. Extensive experimental
results on different benchmarks and a practical pattern recognition problem
with nonlinear, overlapping class boundaries and different feature space dimensions
are provided to show the powerfulness of the proposed method. The
comparative results illustrate that performance of the proposed FGSA-miner
considerably outperforms the standard GSA. Also it is shown that the performance
of the FGSA-miner is comparable to, sometimes better than those of
the CN2 (a traditional data mining method) and similar approach which have
been designed based on other swarm intelligence algorithms (ant colony optimization
and particle swarm optimization) and evolutionary algorithm (genetic


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