Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Proceedings of the 11th Annual conference on Genetic and 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
Hyper-learning for population-based incremental learning in dynamic environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A memory enhanced evolutionary algorithm for dynamic scheduling problems
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
A study on population's diversity for dynamic environments
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
Virtual loser genetic algorithm for dynamic environments
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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In recent years, several memory schemes have been used in Evolutionary Algorithms (EAs) for dynamic optimization problems (DOPs). The Virtual Loser Genetic Algorithm (VLGA), recently proposed, uses a novel type of associative memory to deal with DOPs. This memory scheme memorizes past errors concerning the performed mutations and uses this information to create new individuals when a change in the environment occurs. In this paper VLGA is further investigated in order to enhance its performance in different types of DOPs: the influence of an important parameter is analyzed, and the interaction between the memory scheme and the use of immigrants is also investigated. A novel immigrant scheme is proposed and compared with the random immigrants approach. The results show that the investigated methods significantly enhances the previous version of VLGA for cyclic and random environments.