Prediction in Evolutionary Algorithms for Dynamic Environments Using Markov Chains and Nonlinear Regression



The inclusion of prediction mechanisms in Evolutionary Algorithms (EAs) used to solve dynamic environments allows forecasting the future preparing 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.


Evolutionary Optimization


GECCO 2009, July 2009

Cited by

Year 2013 : 3 citations

 Haobo Fu, Bernhard Sendhoff, Ke Tang, and Xin Yao (2013). Finding robust solutions to dynamic optimization problems. In Applications of Evolutionary Computation, pp. 616-625. Springer Berlin Heidelberg, 2013.

 Carlos RB Azevedo, Fernando J. Von Zuben (2013). Anticipatory Stochastic Multi-Objective Optimization for uncertainty handling in portfolio selection. IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 157-164, IEEE, 2013.

 Uluda?, G., Kiraz, B., Etaner-Uyar, A. ?., & Özcan, E. (2013). A hybrid multi-population framework for dynamic environments combining online and offline learning. Soft Computing, 17(12), 2327-2348.

Year 2012 : 1 citations

 Mathys Cornelius du Plessis (2012). “Adaptive Multi-Population Diferential Evolution for Dynamic Environments”. PhD Thesis, University of Pretoria, 2012.

Year 2011 : 1 citations

 Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011.

Year 2010 : 1 citations

 Di Chio, Cecilia, et al., eds. (2010). Applications of Evolutionary Computation: EvoApplications 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Istanbul, Turkey, April 7-9, 2010, Proceedings. Vol. 1. Springer, 2010.