Design of artificial neural networks based on genetic algorithms to forecast time series

In this work an initial approach to design Artificial Neural Networks to forecast time series is tackle, and the automatic process to design is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks for NN3 Forecasting Time Series Competition are shown

ADANN: Automatic Design of Artificial Neural Networks

Proceeding of: Genetic and Evolutionary Computation Conference, GECCO-08. July 12-16, 2008, Atlanta, Georgia, USA.In this work an improvement of an initial approach to design Artificial Neural Networks to forecast Time Series is tackled, and the automatic process to design Artificial Neural Networks is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. In this approach new information will be included into the chromosome so it will be possible to compare these results with those obtained in a previous approach. There are two principal ideas to take into account: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks to forecast Time Series are shown.The research reported here has been supported by the Ministry of Education and Science under project TRA2007-67374-C02-02

Studying the capacity of grammatical encoding to generate FNN architectures

Proceeding of: 7th International Work-Conference on Artificial and Natural Neuronal Networks. IWANN 2003. June 3-6. Maó, Menorca, Spain.Many methods to codify Artificial Neural Networks have been developed to avoid the defects of direct encoding schema, improving the search into the solution’s space. A method to estimate how the search space is covered and how are the movements along search process applying genetic operators is needed in order to evaluate the different encoding strategies for Feedforward Neural Networks. A first step of this method is considered with two encoding strategies, a direct encoding method and an indirect encoding scheme based on graph grammars: generative capacity, how many different architectures the method is able to generate

Studying the Capacity of Grammatical Encoding to Generate FNN Architectures

Proceeding of: 7th International Work-Conference on Artificial and Natural Neuronal Networks. IWANN 2003. June 3-6. Maó, Menorca, Spain.Many methods to codify Artificial Neural Networks have been developed to avoid the defects of direct encoding schema, improving the search into the solution’s space. A method to estimate how the search space is covered and how are the movements along search process applying genetic operators is needed in order to evaluate the different encoding strategies for Feedforward Neural Networks. A first step of this method is considered with two encoding strategies, a direct encoding method and an indirect encoding scheme based on graph grammars: generative capacity, how many different architectures the method is able to generate