Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Designing evolutionary algorithms for dynamic environments
Designing evolutionary algorithms for dynamic environments
Dynamic optimization by evolutionary algorithms applied to financial time series
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Guest editorial: special section on ant colony optimization
IEEE Transactions on Evolutionary Computation
Perspectives in dynamic optimization evolutionary algorithm
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
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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.