Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning with Genetic Algorithms: An Overview
Machine Learning
Inoculation to Initialise Evolutionary Search
Selected Papers from AISB Workshop on Evolutionary Computing
A genetic algorithm for multiple sequence alignment
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Another investigation on tournament selection: modelling and visualisation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary computation in bioinformatics: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hybrid genetic algorithm for parameter identification of bioprocess models
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
A new greedy randomised adaptive search procedure for multiple sequence alignment
International Journal of Bioinformatics Research and Applications
Flexible case-based retrieval for comparative genomics
Applied Intelligence
A hierarchical parallel genetic approach for the graph coloring problem
Applied Intelligence
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The alignment and comparison of DNA, RNA and Protein sequences is one of the most common and important tasks in Bioinformatics. However, due to the size and complexity of the search space involved, the search for the best possible alignment for a set of sequences is not trivial. Genetic Algorithms have a predisposition for optimizing general combinatorial problems and therefore are serious candidates for solving multiple sequence alignment tasks. Local search optimization can be used to refine the solutions explored by Genetic Algorithms. We have designed a Genetic Algorithm which incorporates local search for this purpose: AlineaGA. We have tested AlineaGA with representative sequence sets of the globin family. We also compare the achieved results with the results provided by T-COFFEE.