Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters
In this paper we present a comparative study of several methods that combine evolutionary
algorithms and local search to optimize multilayer perceptrons: A method that optimizes the …
algorithms and local search to optimize multilayer perceptrons: A method that optimizes the …
Algorithm:: Evolutionary, a flexible Perl module for evolutionary computation
Abstract This paper describes Algorithm:: Evolutionary (A:: E), a Perl module released under
an open source license and designed for the exploration and exploitation of evolutionary …
an open source license and designed for the exploration and exploitation of evolutionary …
Co-evolving multilayer perceptrons along training sets
When designing artificial neural network (ANN) it is important to optimise the network
architecture and the learning coefficients of the training algorithm, as well as the time the …
architecture and the learning coefficients of the training algorithm, as well as the time the …
Evolutionary system for prediction and optimization of hardware architecture performance
The design of computer architectures is a very complex problem. The multiple parameters
make the number of possible combinations extremely high. Many researchers have used …
make the number of possible combinations extremely high. Many researchers have used …
Architecture performance prediction using evolutionary artificial neural networks
The design of computer architectures requires the setting of multiple parameters on which
the final performance depends. The number of possible combinations make an extremely …
the final performance depends. The number of possible combinations make an extremely …
Parallelizing the design of radial basis function neural networks by means of evolutionary meta-algorithms
This work introduces SymbPar, a parallel meta-evolutionary algorithm designed to build
Radial Basis Function Networks minimizing the number of parameters needed to be set by …
Radial Basis Function Networks minimizing the number of parameters needed to be set by …
Cooperative co-evolution of multilayer perceptrons
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 …
neural network and training agorithm parameters, due to its ability to deal with vast search …
Evolved matrix operations for post-processing protein secondary structure predictions
V Aggarwal, RM MacCallum - European Conference on Genetic …, 2004 - Springer
Predicting the three-dimensional structure of proteins is a hard problem, so many have
opted instead to predict the secondary structural state (usually helix, strand or coil) of each …
opted instead to predict the secondary structural state (usually helix, strand or coil) of each …
Comparing hybrid systems to design and optimize artificial neural networks
In this paper we conduct a comparative study between hybrid methods to optimize multilayer
perceptrons: a model that optimizes the architecture and initial weights of multilayer …
perceptrons: a model that optimizes the architecture and initial weights of multilayer …
[PDF][PDF] Optimización paralela multiobjetivo de conjuntos de perceptrones multicapa para clasificación de patrones
PA Castillo, JJ Merelo-Guervós - Inteligencia Artificial. Revista …, 2008 - redalyc.org
En los problemas de clasificación de patrones se busca minimizar el número de patrones
mal clasificados (error de clasificación). Sin embargo, en muchas aplicaciones reales hay …
mal clasificados (error de clasificación). Sin embargo, en muchas aplicaciones reales hay …