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| Abstract: | |
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This work proposes a genetic algorithm (GA) based approach for search of the Pareto optimal set of a multiobjective optimization problem. First the global population is divided into various subpopulations. The algorithm operation consists of two phases: firstly each subpopulation tries to optimize a different objective; later the algorithm searches for good compromise solutions between objectives. Information is exchanged by means of the migration of individuals during the second phase. A weighted sum is used for fitness calculation. Weight vectors are randomly generated for each selection event, wich creates a wide range of search directions. The good behaviour of the proposed algorithm becomes visible in its application to some continuous problems. |
 .: SABIA :. Sistemas Adaptativos y Bioinspirados en Inteligencia Artificial
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