The addition of prediction mechanisms in Evolutionary Algorithms applied to dynamic environments is essential in order to anticipate the changes in the landscape and maximize its adaptability. In previous work, a combination of a linear regression predictor and a Markov chain model was used to enable the EA to anticipate when next change will occur and to introduce useful information before it happens. Since the predicted values, in some situations, are not precise, it's necessary to estimate the associated errors. In this paper we introduce a self adaptable parameter calculated using previously observed errors in the linear predictor. This parameter, called DELTA, assumes that the linear predictor is not exact and some elasticity must be considered when choosing the moment to introduce useful information into the population. In this work we extend previous studies introducing nonlinear change periods to evaluate the prediction's accuracy.
Chen, X., Zhang, D., & Zeng, X. (2015, November). A Stable Matching-Based Selection and Memory Enhanced MOEA/D for Evolutionary Dynamic Multiobjective Optimization. In Tools with Artificial Intelligence (ICTAI), 2015 IEEE 27th International Conference on (pp. 478-485). IEEE.
Yang, S. (2015, July). Evolutionary computation for dynamic optimization problems. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (pp. 629-649). ACM.
Year 2013 : 11 citations
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.
Kisiel-Dorohinicki, M. (2013). Evolutionary multi-agent systems in non-stationary environments. Computer Science, 14.
Byrski, A., & Schaefer, R. (2013). Markov Chain Analysis of Agent-Based Evolutionary Computing in Dynamic Optimization. Procedia Computer Science, 18, 1475-1484.
Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2013). Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 2013.
Hajer Ben-Romdhane, Enrique Alba, and Saoussen Krichen (2013). Best practices in measuring algorithm performance for dynamic optimization problems. Soft Computing, pp. 1-13, Springer, 2013.
Aleksander Byrski, and Robert Schaefer (2013). Markov Chain Analysis of Agent-Based Evolutionary Computing in Dynamic Optimization. Procedia Computer Science 18, pp. 1475-1484, Elsevier, 2013.
Enrique Alba, H. Ben-Romdhane, S. Krichen, B. Sarasola (2013). BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems" Evolutionary Computation for Dynamic Optimization Problems, pp. 171-191, Springer Berlin Heidelberg, 2013.
Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, Xin Yao (2013). Evolutionary Dynamic Optimization: Methodologies Evolutionary Computation for Dynamic Optimization Problems, pp. 39-64, Springer Berlin Heidelberg, 2013.
Hongfeng Wang, Shengxiang Yang (2013). Memetic Algorithms for Dynamic Optimization Problems." Evolutionary Computation for Dynamic Optimization Problems, pp. 137-170, Springer Berlin Heidelberg, 2013.
Shengxiang Yang (2013). Evolutionary computation for dynamic optimization problems. Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, pp. 667-682, ACM, 2013.
Marek Kisiel-Dorohinicki (2013). Evolutionary Multi-Agent Systems In Non-Stationary Environments." Computer Science 14.4, 2013.
Year 2012 : 2 citations
T. T. Nguyen, S. Yang, and J. Branke (2012). “Evolutionary dynamic optimization: A survey of the state of the art”. Swarm and Evolutionary Computation, Elsevier, 2012.
Shengxiang Yang, Yong Jiang, and Trung Thanh Nguyen (2012). "Metaheuristics for dynamic combinatorial optimization problems." IMA Journal of Management Mathematics, 2012.
Year 2011 : 1 citations
Chen Li (2011). Dynamic Optimization Algorithms. Journal of Wuhan University: Natural Science, 2011.
