Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Hybrid Two-Population Genetic Algorithm
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Genetic algorithms as function optimizers
Genetic algorithms as function optimizers
Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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Being based on the theory of evolution and natural selection, the Genetic Algorithms (GA) represent a technique that has been proved as good enough for the resolution of those problems that require a search through a complex space of possible solutions. The maintenance of a population of possible solutions that are in constant evolution may lead to its diversity being lost, consequently it would be more difficult, not only the achievement of a final solution but also the supply of more than one solution The method that is described here tries to overcome those difficulties by means of a modification in traditional GA's. Such modification involves the inclusion of an additional population that might avoid the mentioned loss of diversity of classical GA's. This new population would also provide the piece of exhaustive search that allows to provide more than one solution.