Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Memory based on abstraction for dynamic fitness functions
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Associative memory scheme for genetic algorithms in dynamic environments
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Usually, evolutionary algorithms keep the size of the population fixed. In the context of dynamic environments, many approaches divide the main population into two, one part that evolves as usual another that plays the role of memory of past good solutions. The size of these two populations is often chosen off-line. Usually memory size is chosen as a small percentage of population size, but this decision can be a strong weakness in algorithms dealing with dynamic environments. In this work we do an experimental study about the importance of this parameter for the algorithm's performance. Results show that tuning the population and memory sizes is not an easy task and the impact of that choice on the algorithm's performance is significant. Using an algorithm that dynamically adjusts the population and memory sizes outperforms standard approach.