Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment

  • Authors:

  • Zne-Jung Lee;Shun-Feng Su;Chen-Chia Chuang;Kuan-Hung Liu

  • Affiliations:

  • Department of Information Management, No. 1, Huafan Rd. Shihtin Hsiang, Taipei Hsien 223, Taiwan, ROC;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, ROC;Department of Electrical Engineering, National Ilan University 1, Sec. 1, Shen-Lung Road, I-Lan 260, Taiwan, ROC;Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, ROC

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

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Abstract

Multiple sequence alignment, known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In this paper, we present a novel algorithm of genetic algorithm with ant colony optimization for multiple sequence alignment. The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm (GA) by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment. In the proposed GA-ACO algorithm, genetic algorithm is conducted to provide the diversity of alignments. Thereafter, ant colony optimization is performed to move out of local optima. From simulation results, it is shown that the proposed GA-ACO algorithm has superior performance when compared to other existing algorithms.