HGA-COFFEE: aligning multiple sequences by hybrid genetic algorithm

Authors:
Li-fang Liu;Hong-wei Huo;Bao-shu Wang
Affiliations:
School of Computer Science and Technology, Xidian University, Xi'an, P.R. China;School of Computer Science and Technology, Xidian University, Xi'an, P.R. China;School of Computer Science and Technology, Xidian University, Xi'an, P.R. China
Venue:
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Year:
2005

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Abstract

For multiple sequence alignment problem in molecular biological sequence analysis, a hybrid genetic algorithm and an associated software package called HGA-COFFEE are presented. The COFFEE function is used to measure individual fitness, and five novel genetic operators are designed, a selection operator, two crossover operators and two mutation operators. One of the mutation operators is designed based on the COFFEE's consistency information that can improve the global search ability, and another is realized by dynamic programming method that can improve individuals locally. Experimental results of the 144 benchmarks from the BAliBASE show that the proposed algorithm is feasible, and for datasets in twilight zone and comprising N/C terminal extensions, HGA-COFFEE generates better alignment as compared to other methods studied in this paper.