Solving Minimax Optimization Problem Using Random Crossover Technique in Genetic Algorithm
A min-max optimization problem was originally designed for simultaneous maximization of the same object functions during the same optimization run. In this project, we apply genetic algorithm (GA) technique to solve the min-max problems. So, a great attention was focused on the illustration of how to use genetic algorithms (GA) techniques with random crossover methods in every generation in finding the solution of the min-max problems. According to the obtained results, the proposed methodology of solution reaches the feasible area reasonably slow and consistently and produces relatively bad results. Also shown from the results that depend on using GA for solving min-max problems, we see that the optimum solution can be achieved slowly than traditional GA and notice that the solution is almost equal to the analytical one. Nevertheless, on using the (GA) to solve these min-max problems, we obtain the results slowly.
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