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- W1518202313 abstract "Genetic Algorithms are a search paradigm that applies principles of evolutionary biology (crossover, mutation, natural selection) in order to deal with intractable search spaces. The power and success of GA are mostly achieved by the diversity with the individuals of a population which evolve, in parallel, following the principle of the survival of the fittest. In general, the genetic algorithms resolve combinatorial optimization problems that in (Goldberg, 1989) are mentioned, this implies a large number of responses associated with an exponential growth in solutions potentially feasible according to the magnitude of the problem. In a standard GA the diversity of the individuals is obtained and maintained using the genetic operators crossover and mutation which allow the GA to find feasible solutions and avoid premature convergence to a local maximum (Holland, 1975)." @default.
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- W1518202313 date "2012-03-07" @default.
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- W1518202313 title "Performance of Simple Genetic Algorithm Inserting Forced Inheritance Mechanism and Parameters Relaxation" @default.
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- W1518202313 doi "https://doi.org/10.5772/36208" @default.
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