Journal ArticleDOI
Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment
Zne-Jung Lee,Shun-Feng Su,Chen-Chia Chuang,Kuan-Hung Liu +3 more
- Vol. 8, Iss: 1, pp 55-78
TLDR
The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment and has superior performance when compared to other existing algorithms.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.read more
Citations
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Journal ArticleDOI
Optimum design of hybrid renewable energy systems: Overview of different approaches
Ozan Erdinc,Mehmet Uzunoglu +1 more
TL;DR: In this article, the authors provide a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.
Journal ArticleDOI
A novel collaborative optimization algorithm in solving complex optimization problems
Wu Deng,Huimin Zhao,Huimin Zhao,Li Zou,Li Zou,Li Zou,Guangyu Li,Guangyu Li,Xinhua Yang,Daqing Wu,Daqing Wu +10 more
TL;DR: The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
Journal ArticleDOI
Evolutionary population dynamics and grey wolf optimizer
TL;DR: The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions and is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.
Journal ArticleDOI
Genetic Algorithms, a Nature-Inspired Tool: Survey of Applications in Materials Science and Related Fields
TL;DR: The representative examples selected from recent literature show how broad is the usefulness of this computational method in materials science and related fields.
Journal ArticleDOI
A feature selection method based on modified binary coded ant colony optimization algorithm
TL;DR: Results show that the proposed feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is robust, adaptive and exhibits the better performance than other methods involved in the paper.
References
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Journal ArticleDOI
Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
TL;DR: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved and modifications are incorporated into a new program, CLUSTAL W, which is freely available.
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Ant Colony Optimization
TL;DR: Ant colony optimization (ACO) is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals as discussed by the authors In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization.
Journal ArticleDOI
T-Coffee: A novel method for fast and accurate multiple sequence alignment.
TL;DR: A new method for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives but avoids the most serious pitfalls caused by the greedy nature of this algorithm.