Optimization of multilayer perceptrons using evolutionary algorithms.

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...

G-Prop: Global optimization of multilayer perceptrons using GAs

A general problem in model selection is to obtain the right parameters that make a model "t observed data. For a multilayer perceptron (MLP) trained with back-propagation (BP), this means "nding appropiate layer size and initial weights. This paper proposes a method (G-Prop, genetic backpropagation) that attempts to solve that problem by combining a genetic algorithm (GA) and BP to train MLPs w...

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 ...

Hyperparameter Optimization with Factorized Multilayer Perceptrons

In machine learning, hyperparameter optimization is a challenging task that is usually approached by experienced practitioners or in a computationally expensive brute-force manner such as grid-search. Therefore, recent research proposes to use observed hyperparameter performance on already solved problems (i.e. data sets) in order to speed up the search for promising hyperparameter configuratio...