Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An evolutionary algorithm that constructs recurrent neural networks.

P J Angeline1, G M Saunders, J B Pollack

  • 1Dept. of Comput. and Inf. Sci., Ohio State Univ., Columbus, OH.

IEEE Transactions on Neural Networks
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Intradermal tests with dirofilaria immitis antigen as a diagnostic aid in human filariasis.

United States naval medical bulletin·2010
Same author

Syphilis in School Children of Kingston, Jamaica.

British medical journal·2010
Same author

Long term observation of Plasmodium vivax malaria in the returned serviceman.

United States naval medical bulletin·2010
Same author

Long-term observation of Plasmodium vivax malaria in the returned serviceman.

United States naval medical bulletin·2010
Same author

The treatment of malaria.

Missouri medicine·2010
Same author

Long-term observation of Plasmodium vivax malaria in the returned serviceman.

United States naval medical bulletin·2010
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces GNARL, an evolutionary program for creating recurrent neural networks (RNNs). GNARL overcomes limitations of standard methods by simultaneously evolving both network structure and weights, enabling more complex behaviors.

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Standard methods for recurrent neural network (RNN) induction impose architectural constraints, limiting task adaptability.
  • The complex interplay between RNN structure and function necessitates flexible induction approaches.
  • Evolutionary computation offers powerful search capabilities for complex optimization problems.

Purpose of the Study:

  • To address the limitations of current RNN induction methods.
  • To propose and evaluate an evolutionary program for simultaneous structure and weight acquisition in RNNs.
  • To enable the emergence of complex behaviors and topologies not typically captured by standard methods.

Main Methods:

  • Developed GNARL, an evolutionary program employing evolutionary programming for RNN acquisition.

Related Experiment Videos

  • Utilized a population-based search strategy to simultaneously evolve network structure and weights.
  • Contrasted the proposed evolutionary approach with standard genetic algorithms for network induction.

    • Main Results:

    • Demonstrated the inapplicability of genetic algorithms for network acquisition tasks.
    • Showcased GNARL's capability to simultaneously acquire both structure and weights of recurrent networks.
    • Highlighted the emergence of complex behaviors and topologies through GNARL's empirical acquisition method.

    Conclusions:

    • Evolutionary programming, as implemented in GNARL, is a suitable method for recurrent neural network induction.
    • GNARL overcomes the architectural limitations of standard network induction techniques.
    • The proposed method facilitates the discovery of novel and complex network architectures and behaviors.