Abstract
In Computational biology, Biological sequence alignment plays an essential role in gene structure/function prediction. Sequence alignment is a problem of optimization that finds optimal arrangement of sequences by maximizing the similarities of residues. Metaheuristics are the modern search and optimization techniques providing competitive solutions for many real world problems. However the techniques suffer from the efficient evolution operators and control parameters. Differential Evolution is a stochastic real parameter optimization technique with few control parameters. This paper proposes an Advanced Differential Evolution (ADE) algorithm with a new mutation operator “ADE/best-worst-rand/1” considering least and best fittest candidate solutions. The performance of the algorithm is evaluated using various data sets and compared with other evolutionary algorithms, Genetic Algorithm (GA) and Differential Evolution. Experimental results have shown the efficiency and prominence of new proposed algorithm in producing best solutions for the sequence alignment with improved fitness. It is observed that performance improvement of ADE over DE is nearly 1.22 and 16.32 % over GA.
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Gavarraju, L.N.J., Pujari, J.J., Karteeka Pavan, K. (2016). Sequence Alignment by Advanced Differential Evolutionary Algorithm. In: Lakshmi, P., Zhou, W., Satheesh, P. (eds) Computational Intelligence Techniques in Health Care. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-10-0308-0_6
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DOI: https://doi.org/10.1007/978-981-10-0308-0_6
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