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Abstract
First part of this work consists of basic information about Genetic algorithm like what are Individual, Population, Crossover, Genes, Binary Encoding, Flipping, Crossover probability Mutation probability. What is it used for, what is their aim. A genetic algorithm is one of a class of algorithms that searches a solution space for the optimal solution to a problem. In this article the methods of selection, crossover and mutation are specified. In the second part, solving a maximizing problem using Genetic algorithm in a single generation. A single generation of a Genetic algorithm is performed here with encoding, selection, crossover and mutation.
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