Performance of Time-Varying Particle Swarm Optimizer to Predict Cancers
Classification of tumors is a challenging task in the field of bioinformatics. The gene expression levels measured using microarray approach contains thousands of levels. Finding optimum number of genes expression levels to classify tumor samples is carried out in this paper using PSO. The conventional PSO algorithm works with constant social and cognitive coefficients. This paper proposes time-varying PSO in which the social and cognitive coefficients are allowed to vary with respect to time. The performance of the proposed particle swarm optimizer gives better results when compared to the conventional PSO in classifying the tumor samples.
KeywordsParticle swarm optimization Time-varying coefficients Tumor classification Gene expression levels
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