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• W1573449042 abstract "Inspired by the Darwinian principles of evolution, genetic algorithms (GAs) (Holland, 1975, Goldberg, 1989) are widely used in optimization. GAs encode all the variables corresponding to a candidate solution (the phenotype) in a data structure (the genotype) and maintain a population of genotypes which is evolved mimicking Nature's evolutionary process. Individuals are selected for reproduction in a way that better performing solutions have a higher probability of being selected. The genetic material contained in the chromosome of such parent individuals is then recombined and mutated, by means of crossover and mutation operators, giving rise to offspring which will form a new generation of individuals. The process is repeated for a given number of generations or until some stopping criteria are met. GAs are search algorithms which can be dire ctly applied to unconstrained optimization problems, UOP ) , ( S f , where one seeks for an element x belonging to the search space S , which minimizes (or maximizes) the real-valued objective function f . In this case, the GA usually employs a fitness function closely related to f . However, practical optimization problems often involve several types of constraints and one is led to a constrained optimization problem, COP )" @default.
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• W1573449042 date "2008-04-01" @default.
• W1573449042 modified "2023-10-01" @default.
• W1573449042 title "An Adaptive Penalty Method for Genetic Algorithms in Constrained Optimization Problems" @default.
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• W1573449042 doi "https://doi.org/10.5772/5446" @default.
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