Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Neural Networks in Finance: Gaining Predictive Edge in the Market (Academic Press Advanced Finance Series)
Evolutionary Computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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Artificial Neural Networks (ANN) have been widely used in order to solve the time series forecasting problem. One of its most promising approaches is the combination with other intelligence techniques, as genetic algorithms, evolutionary strategies, etc. The efficiency of these technics, if used correctly, can be very high. Unfortunately, in terms of fitness function, there is still some lacks of experimental (and theoretical) results to help the practitioners to use these technics in order to find better predictions. This paper proposes others fitness functions (instead of conventional MSE based) and presents an experimental investigation of eight different fitness functions for time series prediction based on five well known measures of statistical performance in the literature. Using a hybrid method for tuning of the ANN structure and parameters (a modified genetic Algorithm), an analysis of the final results effects are made according with four relevant time series. This work shows that small changes of the fitness function evaluation can lead to a significantly improved performance.