An artificial neural-network genetic based approach for time series forecasting - Neves, José (1997)
Genetic Algorithms (GAs) are a class of very general optimization procedures, a class of randomized optimization heuristics based loosely on the biological paradigm of natural selection. Artificial Neural Networks (ANNs) are well established procedures in the domains of pattern recognition and function approximation, where their properties and training methods have been well studied. Recently, there have been some successful applications of ANNs in a new setting, that of sequential decision making under uncertainty (or stochastic control), where one's goal is the cost-to-go or value function, which evaluates and guides management or control decisions in an organization. In this work one reports on the integration of GAs and ANNs, in terms of a new paradigm, the GANN's one, which will be applied to forecasts of sun spots, airline passengers and yields from batch chemical processes.