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Genetic Algorithms |
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Genetic Algorithms Models that optimize rules by mimicking the Darwinian Law of survival of the fittest. A set of rules are chosen by those that work the best. The weakest are discarded. In addition, two successful rules can be combined (the equivalent to genetic cross-overs) to produce offspring rules. The offspring can replace the parents, or they will be discarded if less successful than the parents. Mutation is also accomplished by randomly changing elements. Mutation and cross-over occur with low probability, as in nature. |
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In essence it represents a new form of genetic algorithm aimed at networks in which mutations occur in each cycle as either base-pair switches or large translocations. The list of enhancements includes a new efficient exploratory optimization technique--Multi-Island Genetic Algorithm, an updated Adaptive Simulated Annealing technique, and approximation model enhancements addition of Kriging and 3rd and 4th order Response Surface Models. In-silico computational biology: In-silico ADMET (Absorbtion, Distribution, Metabolism, Excretion, Toxicology) prediction based on genetic algorithm neural network software |
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Genetic algorithm
