Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Two Different Elitism Operators for Stochastic and Deterministic Applications
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
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Multiple alignments of biological nucleic acid sequences are one of the most commonly used techniques in sequence analysis. These techniques demand a big computational load. We present a Genetic Algorithms (GA) that optimizes an objective function that is a measure of alignment quality (distance). Each individual in the population represents (in an efficient way) some underlying operations on the sequences and they evolve, by means of natural selection, to better populations where they obtain better alignment of the sequences. The improvement of the effectiveness is obtained by an elitism operator specially designed and by initial bias given to the population by the background knowledge of the user. Our GA presents some characteristics as robustness, convergence to solution, extraordinary capability of generalization and a easiness of being coded for parallel processing architectures, that make our GA very suitable for multiple molecular biology sequences analysis.