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The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. In this repository we will try to apply genetic algorithms to some of the famous problems - of course there are those who have solved these problems before - we are just shifting our understanding of the working method applied to many of these forms and also the difference between each project between them.
