An approach to anytime learning
ML92 Proceedings of the ninth international workshop on Machine learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Enhancing the virtual loser genetic algorithm for dynamic environments
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The use of mechanisms that generate and maintain diversity in the population was always seen as fundamental to help Evolutionary Algorithms to achieve better performances when dealing with dynamic environments. In the last years, several studies showed that this is not always true and, in some situations, too much diversity can hinder the performance of the Evolutionary Algorithms dealing with dynamic environments. In order to have more insight about this important issue, we tested the performance of four types of Evolutionary Algorithms using different methods for promoting diversity. All the algorithms were tested in cyclic and random dynamic environments using two different benchmark problems. We measured the diversity of the population and the performances obtained by the algorithms and important conclusions were obtained.