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Genetic Algorithm Optimization of Photonic Band gap Structures by George A. Amazonas, Daniel S. Wale, Raymond Wildman, and Andrea MohanARLRP130September 2006A reprint from the International Journal

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Understand the problem you want to solve using genetic algorithm optimization.

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Apply genetic operators such as crossover and mutation to create new offspring.

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Repeat the previous steps for a certain number of generations or until convergence.

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Retrieve the best solution found as the optimal result of the genetic algorithm optimization.

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It is a method used to optimize solutions to complex problems by mimicking the process of natural selection.

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The specific problem being addressed, the parameters and constraints of the problem, the fitness function used to evaluate solutions, and the results of the optimization process.

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