In bioinformatics, sequence alignment is the heart of the sequence analysis. Sequence can be aligned locally or globally depending upon the biologist's need for the analysis. As local sequence alignment is considered important, there is demand for an efficient algorithm. Due to the enormous sequences in the biological database, there is a trade-off between computational time and accuracy. In general, all biological problems are considered as computational intensive problems. To solve these kinds of problems, evolutionary-based algorithms are proficiently used. This chapter focuses local alignment in molecular sequences and proposes an improvised hybrid evolutionary algorithm using particle swarm optimization and cellular automata (IPSOCA). The efficiency of the proposed algorithm is proved using the experimental analysis for benchmark dataset BaliBase and compared with other state-of-the-art techniques. Using the Wilcoxon matched pair signed rank test, the significance of the proposed algorithm is explicated.
BALSA: Bayesian algorithm for local sequence alignment
