thesis

MSG: A Gap-Oriented Genetic Algorithm for Multiple Sequence Alignment

Abstract

Traditional Multiple Sequence Alignment (MSA) Algorithms are deterministic. Genetic algorithms for protein MSA have been documented. However, these are not able to exceed in all cases the scores obtained by Clustal­W, the freely available de­facto standard. My solution, called “MSG”, places gaps rather than amino acids. The algorithm is multi­tribal, uses only a few very simple operators with adaptive frequencies, and jumpstarts one population from the Clustal­W solution. Results are reported for 14 data sets, on all of which MSG exceeds the Clustal­W score

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