Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
Associative memory scheme for genetic algorithms in dynamic environments
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
The effect of selection from old populations in genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A method to reuse old populations in genetic algorithms
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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Recently, Evolutionary Algorithms (EAs) with associative memory schemes have been developed to solve Dynamic Optimization Problems (DOPs). Current associative memory schemes always retrieve both the best memory individual and the corresponding environmental information. However, the memory individual with the best fitness could not be the most appropriate one for new environments. In this paper, two novel associative memory retrieving strategies are proposed to obtain the most appropriate memory environmental information. In these strategies, two best individuals are first selected from the two best memory individuals and the current best individual. Then, their corresponding environmental information is evaluated according to either the survivability or the diversity, one of which is retrieved. In experiments, the proposed two strategies were embedded into the state-of-the-art algorithm, i.e. the MPBIL, and tested on three dynamic functions in cyclic environments. Experiment results demonstrate that the proposed retrieving strategies enhance the search ability in cyclic environments.