Real world problems often present multiple, frequently conflicting, objectives. The research for optimal solutions of multi-objective problems can be achieved through means of genetic algorithms, which are inspired by the natural process of evolution: an initial population of solutions is randomly generated, then pairs of solutions are selected and combined in order to create new solutions slightly different the initial solutions. The fittest solutions are kept in the population and are used to generate new solutions

devooght_2011.pdf

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