ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies

Abstract

In this paper we present ELeaRNT an evolutionary strategy which evolves rich neural network topologies in order to find an optimal domain specific non linear function approximator with a good generalization performance. The neural networks evolved by the algorithm have a feed forward topology with shortcut connections and arbitrary activation functions at each layer. This kind of topologies has not been thoroughly investigated in literature, but is particularly well suited for non linear regression tasks. The experimental results prove that, in such tasks, our algorithm can build, in a completely automated way, neural network topologies able to outperform classic neural network models designed by hand. Also when applied to classification problems, the performance of the obtained neural networks is fully comparable to that of classic neural networks and in some cases noticeably better.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA456062

Entities

People

  • Matteo Matteucci

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Data Sets
  • Genetic Algorithms
  • Genetics
  • Learning
  • Machine Learning
  • Network Architecture
  • Network Topology
  • Neural Networks
  • Real Variables
  • Topology
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neuroscience
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks