Skip to main content
SpringerLink
Log in

Supplanting Neural Networks with ODEs in Evolutionary Robotics

  • Conference paper
Simulated Evolution and Learning (SEAL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6457))

Included in the following conference series:

  • 2719 Accesses

Abstract

A new approach to evolutionary robotics is presented. Neural networks are abstracted and supplanted by a system of ordinary differential equations that govern the changes in controller outputs. The equations are evolved as trees using an evolutionary algorithm based on symbolic regression in genetic programming. Initial proof-of-concept experiments are performed using a simulated two-wheeled robot that must drive a straight line while wheel response properties vary. Evolved controllers demonstrate the ability to learn and adapt to a changing environment, as well as the ability to generalize and perform well in novel situations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Federici, D., Downing, K.: Evolution and Development of a Multicellular Organism: Scalability, Resilience, and Neutral Complexification. Artificial Life 12, 381–409 (2006)

    Article  Google Scholar 

  2. Floreano, D., Mondada, F.: Evolution of Plastic Neurocontrollers for Situated Agents. In: 4th International Conference on Simulation of Adaptive Behavior. MIT Press, MA (1996)

    Google Scholar 

  3. Poli, R., Langdon, W.B., Mcphee, N.F.: A Field Guide to Genetic Programming (2008), Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk

  4. Soltoggio, A., Durr, P., Mattiussi, C., Floreano, D.: Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems. In: IEEE Congress on Evolutionary Computation, pp. 2471–2478. IEEE Press, Los Alamitos (2007)

    Google Scholar 

  5. Stanley, K.O., Miikkulainen, R.: Evolving Neural Networks Through Augmenting Topologies. Evolutionary Computation 10, 99–127 (2002)

    Article  Google Scholar 

  6. Stanley, K.O.: Compositional Pattern Producing Networks: A Novel Abstraction of Development. Genetic Programming and Evolvable Machines Special Issue on Developmental Systems 8, 131–162 (2007)

    Article  Google Scholar 

  7. Thangavelautham, J., D’Eleuterio, G.M.T.: A Coarse-Coding Framework for a Gene-Regulatory-Based Artificial Neural Tissue. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 67–77. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  8. Wieland, A.: Evolving Neural Network Controllers for Unstable Systems. In: Proceedings of the International Joint Conference on Neural Networks, pp. 667–673. IEEE Press, Los Alamitos (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Toronto Institute for Aerospace Studies, 4925 Dufferin Street, Toronto, Ontario, Canada

    Paul Grouchy & Gabriele M. T. D’Eleuterio

Authors
  1. Paul Grouchy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriele M. T. D’Eleuterio
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  2. Department of Computer Science andl Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Arnab Bhattacharya

  3. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, 712302, Kharagpur, West Bengal, India

    Nirupam Chakraborti

  4. Department of Civil Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Partha Chakroborty  & Ashu Jain  & 

  5. Department of Electronics and Communication Engineering, Jadavpur University, 700032, Kolkata, West Bengal, India

    Swagatam Das

  6. Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Joydeep Dutta

  7. Department of Chemical Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, Uttar Pradesh, India

    Santosh K. Gupta

  8. Aspiring Minds, 24 Pusa Road, 110005, New Delhi, India

    Varun Aggarwal

  9. Warwick Business School, University of Warwick, CV4 7AL, Coventry, UK

    JĂĽrgen Branke

  10. Department of Computer Science and Engineering, University of Nevada, 89557, Reno, NV, USA

    Sushil J. Louis

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Grouchy, P., D’Eleuterio, G.M.T. (2010). Supplanting Neural Networks with ODEs in Evolutionary Robotics. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17298-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17297-7

  • Online ISBN: 978-3-642-17298-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation