Abstract
The execution of many computational steps per time unit typical of parallel computers offers an important benefit in reducing the computing time in real world applications. In this work, a parallel Particle Swarm Optimization (PSO) is used for gene selection of high dimensional Microarray datasets. The proposed algorithm, called PMSO, consists of running a set of independent PSOs following an island model, where a migration policy exchanges solutions with a certain frequency. A feature selection mechanism is embedded in each subalgorithm for finding small samples of informative genes amongst thousands of them. PMSO has been experimentally assessed with different population structures on four well-known cancer datasets. The contributions are twofold: our parallel approach is able to improve sequential algorithms in terms of computational time/effort (Efficiency of 85%), as well as in terms of accuracy rate, identifying specific genes that our work suggests as significant ones for an accurate classification. Additional comparisons with several recent state the of art methods also show competitive results with improvements of over 100% in the classification rate and very few genes per subset.
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