Multiobjective Programming With Continuous Genetic Algorithm
Keywords: Chromosome, Crossover, Heuristics, Mutation, Optimization, Population, Ranking,Genetic Algorithms, Multi-Objective, Pareto Optimal Solutions, Parent selection.
Abstract: Nowadays, we want to have a good life, which may mean more wealth, more power, more respect and more time for our selves, together with a good health and a good second generation, etc. Indeed, all important political, economical and cultural events have involved multiple criteria in their evolution. Multiobjective optimization deals with the investigation of optimization problems that possess more than one objective function. Usually there is no single solution that optimizes all functions simultaneously, we have solution set that is called nondominated set and elements of this set are usually infinite. It is from this set decision is made by taking elements of nondominated set as alternatives, which is given by analysts. But practically extraction of nondominated solutions and setting fitness function are difficult. This Paper will try to solve problems that the decision maker face in extraction of Pareto optimal solution with continuous variable genetic algorithm and taking objective function as fitness function without modification by considering box constraint and generating initial solution within box constraint and penalty function for constrained one. Solutions will be kept in feasible region during mutation and recombination.
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