Paper Info
Title
An Intelligent System for Multiple Sequences Alignment
Abstract
This paper provides an intelligent system, a fuzzy-genetic algorithm (FGA) with local search, for multiple sequences alignment. The general multiple sequence alignment, known as NP-hard problem, refers to search for maximal similarity in three or more sequences. The proposed algorithm is to enhance the performance of genetic algorithm by incorporating local search and fuzzy set theory for multiple sequence alignment. In the proposed algorithm, genetic algorithms perform a multiple directional search by maintaining a set of solutions. Local search operators are performed to explore the neighborhood in an attempt to enhance the fitness of the solution in a local manner. Moreover, fuzzy set theory is designed to dynamically adjust the probability of crossover, mutation and local search during evolutionary process. Results from our experiments indicate that our approach can obtain good performance in the majority of data sets with both low similarity and high diversity
Year
DOI
Venue
2005
10.1109/ICSMC.2005.1571283
SMC
Keywords
Field
DocType
fuzzy set theory,knowledge based systems,multiple directional search,sequence alignment,genetic algorithm,multiple sequences alignment,np-hard problem,fuzzy logic,search problems,biology computing,sequences,computational complexity,intelligent system,genetic algorithms,local search operators,local search,fuzzy-genetic algorithm,probability,multiple sequence alignment,np hard problem
Hill climbing,Search algorithm,Guided Local Search,Computer science,Beam search,Artificial intelligence,Local search (optimization),Multiple sequence alignment,Best-first search,Machine learning,Genetic algorithm
Conference
Volume
ISBN
Citations 
2
0-7803-9298-1
0
PageRank 
References 
Authors
0.34
1
5
Name
Order
Citations
PageRank
Zne-Jung Lee194043.45
Chou-Yuan Lee229517.36
Huei-Lung Yu300.34
Kuan-Hung Liu4573.16
Shun-Feng Su5119497.62