Evolving neural networks

Miikkulainen, Risto

doi:10.1145/2598394.2605346

Cited by 3 publications

(1 citation statement)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [14] automated method for optimizing deep learning architectures through evolution is proposed, extending existing neuroevolution methods. Authors of [15] sketch a genetic approach for evolving a deep autoencoder network enhancing the sparsity of the synapses by means of special operators.…”

Section: Introductionmentioning

confidence: 99%

Evolving KERAS Architectures for Sensor Data Analysis

Vidnerová

Neruda

2017

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance.This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization of a network architecture. The algorithm is inspired by and designed directly for the Keras library [1] that is one of the most common implementations of deep neural networks.The target application is the prediction of air pollution based on sensor measurements. The proposed algorithm is evaluated on experiments on sensor data and compared to several fixed architectures and support vector regression.

show abstract

Section: Introductionmentioning

confidence: 99%

Evolving KERAS Architectures for Sensor Data Analysis

Vidnerová

Neruda

2017

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

Improving Neuroevolution with Complementarity-Based Selection Operators

Maul

2016

Neural Process Lett

View full text Add to dashboard Cite

Metaheuristic algorithm to train product and sigmoid neural network classifiers

Tallón‐Ballesteros

2019

Expert Systems

View full text Add to dashboard Cite

This paper develops three frameworks based on a metaheuristic algorithm to train neural network classifiers. The architecture is a single‐hidden‐layer feedforward network. The first methodology spreads a base configuration over the nodes of a computing cluster; each of them executes the same algorithm to train the neural network with a different parameter setting. The second approach does a refined training via a biphase metaheuristic algorithm to maintain the diversity a period longer than the usual; it may be run in a sequential or distributed way. The third framework performs a data preparation phase by means of feature subset selection to reduce the number of inputs to the biphase metaheuristic algorithm. The two first methodologies have been tested using a complete test bed with product and unipolar sigmoid units in the hidden layer, and the statistical tests reveal that product nodes are significantly the most accurate. The third framework has included four feature subset selectors with different properties to reduce the number of inputs to the product unit artificial neural network, and the nonstatistical test shed light on that the results with a preprocessing phase are significantly more accurate than the results with the raw data.

show abstract

Evolving neural networks

Cited by 3 publications

References 76 publications

Evolving KERAS Architectures for Sensor Data Analysis

Evolving KERAS Architectures for Sensor Data Analysis

Improving Neuroevolution with Complementarity-Based Selection Operators

Metaheuristic algorithm to train product and sigmoid neural network classifiers

Contact Info

Product

Resources

About