Parallel genetic algorithms with a continuity operator that allows for knowledge inclusion

In recent years we introduced a continuity operator, the "Superindividual", that allows for the inclusion of knowledge in the evolution of the genetic algorithm. Since we deal with very complex optimization problems, we developed a parallel genetic algorithm, with the Superindividual operator. The paper presents this parallel algorithm, which improves on the results of the conventional genetic algorithm. Two different models of parallel genetic algorithms are compared. The results are very encouraging.