Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Tracking moving optima using kalman-based predictions
Evolutionary Computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IEEE Transactions on Evolutionary Computation
Memory-based CHC algorithms for the dynamic traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
CHC-based algorithms for the dynamic traveling salesman problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Virtual loser genetic algorithm for dynamic environments
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Enhancing the virtual loser genetic algorithm for dynamic environments
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Finding robust solutions to dynamic optimization problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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The inclusion of prediction mechanisms in Evolutionary Algorithms (EAs) used to solve dynamic environments allows forecasting the future and this way we can prepare the algorithm to the changes. Prediction is a difficult task, but if some recurrence is present in the environment, it is possible to apply statistical methods which use information from the past to estimate the future. In this work we enhance a previously proposed computational architecture, incorporating a new predictor based on nonlinear regression. The system uses a memory-based EA to evolve the best solution and a predictor module based on Markov chains to estimate which possible environments will appear in the next change. Another prediction module is responsible to estimate when next change will happen. In this work important enhancements are introduced in this module, replacing the linear predictor by a nonlinear one. The performance of the EA is compared using no prediction, using predictions supplied by linear regression and by nonlinear regression. The results show that this new module is very robust allowing to accurately predicting when next change will occur in different types of change periods.