Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Motif Discovery Problem

The computational discovery of DNA motifs for previously uncharacterized transcription factors in groups of co-regulated genes is a well-studied problem with a great deal of practical relevance to the biologist. In this paper, we applied an improved hybridization of adaptive Biogeography-Based Optimization (ABBO) with differential evolution (DE) approach, namely ABBO/DE/GEN, to predict motifs from DNA sequences. ABBO/DE/GEN adaptively changes migration probability and mutation probability based on the relation between the cost of fitness function and average cost every generation, and the mutation operators of BBO are modified based on DE algorithm and the migration operators of BBO are modified based on number of iteration to meet motif discovery requirements. Hence it can generate the promising candidate solutions. Statistical comparisons with some typical existing approaches on three commonly used datasets are provided, which demonstrates the validity and effectiveness of the ABBO/DE/GEN algorithm. Compared with BBO/DE/GEN approaches, ABBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions. Copyright © 2014 IFSA Publishing, S. L.