The recent advent of bioinformatics has given rise to the central and recurrent problem
of optimally aligning biological sequences. Many techniques have been proposed in an
attempt to solve this complex problem with varying degrees of success. This thesis
investigates the application of a computational intelligence technique known as particle
swarm optimization (PSO) to the multiple sequence alignment (MSA) problem. Firstly,
the performance of the standard PSO (S-PSO) and its characteristics are fully analyzed.
Secondly, a scalability study is conducted that aims at expanding the S-PSO’s application
to complex MSAs, as well as studying the behaviour of three other kinds of PSOs on the
same problems. Experimental results show that the PSO is efficient in solving the MSA
problem and compares positively with well-known CLUSTAL X and T-COFFEE.
©University of Pretoria 2007
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