A statistical model of pollution-caused pulmonary crises

Authors:
Daniel Rodríguez-Pérez;Jose L. Castillo;J. C. Antoranz
Affiliations:
Departamento de Física Matemática y Fluidos, UNED, Madrid, Spain;Departamento de Física Matemática y Fluidos, UNED, Madrid, Spain;Departamento de Física Matemática y Fluidos, UNED, Madrid, Spain
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 11
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Combining evolution with credit apportionment: a new learning algorithm for neural nets

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Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Optimisation of Multilayer Perceptrons Using a Distributed Evolutionary Algorithm with SOAP

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Distributed Coevolutionary Genetic Algorithms for Multi-Criteria and Multi-Constraint Optimisation

Selected Papers from AISB Workshop on Evolutionary Computing
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents

Evolutionary Computation
Co-evolutionary learning of neural networks

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
New methods for competitive coevolution

Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution

Evolutionary Computation
Coevolutionary computation

Artificial Life
Constructive neural-network learning algorithms for pattern classification

IEEE Transactions on Neural Networks
Statistical analysis of the parameters of a neuro-genetic algorithm

IEEE Transactions on Neural Networks

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Abstract

Co-evolution is a posible solution to the problem of simultaneous optimization of artificial neural network and training agorithm parameters, due to its ability to deal with vast search spaces. Moreover, this scheme is recommendable when the optimization problem is decomposable in subcomponents. In this paper an approach to cooperative co-evolutionary optimisation of multilayer perceptrons, that improves the G-Prop genetic back-propagation algorithm, is presented. Obtained results show that this co-evolutionary version of G-Prop obtains similar or better results needing much fewer training epochs and thus using much less time than the sequential versions.