G-Prop: Global optimization of multilayer perceptrons using GAs

G-Prop: Global Optimization of Multilayer Perceptrons using GAs

G-Prop-II: Global Optimization of Multilayer Perceptrons using GAs

A general problem in model selection is to obtain the right parameters that make a model fit observed data. For a Multilayer Perceptron (MLP) trained with Backpropagation (BP), this means finding the right hidden layer size, appropriate initial weights and learning parameters. This paper proposes a method (G-Prop-II) that attempts to solve that problem by combining a genetic algorithm (GA) and ...

G - Prop - III : Global Optimization of Multilayer Perceptrons using anEvolutionary

This paper proposes a new version of a method (G-Prop-III, genetic backpropaga-tion) that attempts to solve the problem of nding appropriate initial weights and learning parameters for a single hidden layer Mul-tilayer Perceptron (MLP) by combining a genetic algorithm (GA) and backpropagation (BP). The GA selects the initial weights and the learning rate of the network, and changes the number o...

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 parallel approach to optimize the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolut...

SA - Prop : Optimization of Multilayer PerceptronParameters using Simulated

A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, terme...