Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Genetic and Evolutionary Algorithms for Time Series Forecasting
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Topology aware internet traffic forecasting using neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Evolving sparsely connected neural networks for multi-step ahead forecasting
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
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Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.