Abstract
The Dynamical Optimization Evolutionary Algorithms (DOEAs) have been applied to solve Dynamical Optimization Problems which are very common in real-world applications. But little work focused on the convergent DOEAs. In this paper new definitions of convergence are proposed and a new algorithm named Vector Prediction Approach is designed. This algorithm firstly analyzes the genes of best individuals from the past, then predicts the next genes of best individual in every tick by Gene Programming, such that the algorithm tracks the optima when time varying. The numerical experiments on two test-bed functions show that this algorithm can track the optima when time varying. The convergence of this algorithm under certain conditions is proved.
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Zheng, B., Li, Y., Hu, T. (2006). Vector Prediction Approach to Handle Dynamical Optimization Problems. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_45
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DOI: https://doi.org/10.1007/11903697_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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