Skip to main content
SpringerLink
Log in

A Comparison of Multiobjective Algorithms in Evolving Quadrupedal Gaits

  • Conference paper
  • First Online:
Book cover From Animals to Animats 14 (SAB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9825))

Included in the following conference series:

Abstract

Robotic systems, whether physical or virtual, must balance multiple objectives to operate effectively. Beyond performance metrics such as speed and turning radius, efficiency of movement, stability, and other objectives contribute to the overall functionality of a system. Optimizing multiple objectives requires algorithms that explore and balance improvements in each. In this paper, we evaluate and compare two multiobjective algorithms, NSGA-II and the recently proposed Lexicase selection, investigating distance traveled, efficiency, and vertical torso movement for evolving gaits in quadrupedal animats. We explore several variations of Lexicase selection, including different parameter configurations and weighting strategies. A control treatment evolving solely on distance traveled is also presented as a baseline. All three algorithms (NSGA-II, Lexicase, and Control) produce effective locomotion in the quadrupedal animat, but differences arise in performance and efficiency of movement. The NSGA-II algorithm significantly outperforms Lexicase selection in all three objectives, while Lexicase selection significantly outperforms the control in two of the three objectives.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Ackerman, J., Seipel, J.: Energy efficiency of legged robot locomotion with elastically suspended loads. IEEE Trans. Robot. 29(2), 321–330 (2013)

    Article  Google Scholar 

  2. Auerbach, J.E., Bongard, J.C.: Environmental Influence on the Evolution of Morphological Complexity in Machines. PLoS Comput. Biol. 10(1), e1003399 (2014)

    Article  Google Scholar 

  3. Beer, R.D.: Toward the evolution of dynamical neural networks for minimally cognitive behavior. In: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, vol. 1, pp. 421–429. MIT Press (1996)

    Google Scholar 

  4. Brooks, R.A.: A robot that walks; emergent behaviors from a carefully evolved network. Neural Comput. 1(2), 253–262 (1989)

    Article  Google Scholar 

  5. Cliff, D., Husbands, P., Harvey, I.: Explorations in evolutionary robotics. Adapt. Behav. 2(1), 73–110 (1993)

    Article  Google Scholar 

  6. Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. In: Proceedings of the IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 2764–2771 (2009)

    Google Scholar 

  7. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  8. Doncieux, S., Mouret, J.B.: Behavioral diversity with multiple behavioral distances. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation, pp. 1427–1434. IEEE, Cancun (2013)

    Google Scholar 

  9. Floreano, D., Husbands, P., Nolfi, S.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 1423–1451. Springer, Berlin (2008)

    Chapter  Google Scholar 

  10. Gomez, F., Miikkulainen, R.: Active guidance for a finless rocket using neuroevolution. In: Proceedings of the 2003 Genetic and Evolutionary Computation Conference, Chicago, Illinois, USA, pp. 2084–2095 (2003)

    Google Scholar 

  11. Helmuth, T., Spector, L., Matheson, J.: Solving uncompromising problems with Lexicase selection. In: IEEE Transactions on Evolutionary Computation, vol. 99, p. 1 (2014)

    Google Scholar 

  12. Koos, S., Mouret, J.B., Doncieux, S.: Crossing the reality gap in evolutionary robotics by promoting transferable controllers. In: Proceedings of the 2010 ACM Genetic and Evolutionary Computation Conference, pp. 119–126. ACM, Portland (2010)

    Google Scholar 

  13. Lehman, J., Stanley, K.O.: Efficiently evolving programs through the search for novelty. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 837–844. ACM, Portland (2010)

    Google Scholar 

  14. Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 829–836. Morgan Kaufmann Publishers, New York (2002)

    Google Scholar 

  15. Mouret, J.-B.: Novelty-based multiobjectivization. In: Doncieux, S., Bredèche, N., Mouret, J.-B. (eds.) New Horizons in Evolutionary Robotics. SCI, vol. 341, pp. 139–154. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  16. Ollion, C., Doncieux, S.: Towards behavioral consistency in neuroevolution. In: Ziemke, T., Balkenius, C., Hallam, J. (eds.) SAB 2012. LNCS, vol. 7426, pp. 177–186. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Paul, C.: Morphological computation: a basis for the analysis of morphology and control requirements. Robot. Auton. Syst. 54(8), 619–630 (2006). http://www.sciencedirect.com/science/article/pii/S0921889006000613

    Article  Google Scholar 

  18. Paul, C., Bongard, J.C.: The road less travelled: morphology in the optimization of biped robot locomotion. In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, Hawaii, USA, pp. 226–232 (2001)

    Google Scholar 

  19. Schrum, J., Miikkulainen, R.: Evolving multimodal networks for multitask games. IEEE Trans. Comput. Intell. AI Games 4(2), 94–111 (2012)

    Article  Google Scholar 

  20. Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22 (1994)

    Google Scholar 

  21. Smith, R.: Open Dynamics Engine (2013). http://www.ode.org/

  22. Spector, L.: Assessment of problem modality by differential performance of Lexicase selection in genetic programming: a preliminary report. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 401–408. ACM, Philadelphia (2012)

    Google Scholar 

  23. Szerlip, P., Stanley, K.O.: Indirectly encoded sodarace for artificial life. In: Proceedings of the 12th European Conference on Artificial Life, Taormina, Italy, pp. 218–225 (2013)

    Google Scholar 

  24. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. Technical report, Swiss Federal Institute of Technology (ETH), Zurich (2001)

    Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the contributions and feedback provided by Anthony Clark, Xiaobo Tan, Craig McGowan, and members of the BEACON Center at Michigan State University. This work was supported in part by National Science Foundation grants CNS-1059373, CNS-0915855, and DBI-0939454, and by a grant from Michigan State University.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Jared M. Moore & Philip K. McKinley

Authors
  1. Jared M. Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philip K. McKinley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philip K. McKinley .

Editor information

Editors and Affiliations

  1. Aberystwyth University, Aberystwyth, United Kingdom

    Elio Tuci

  2. Aberystwyth University, Aberystwyth, United Kingdom

    Alexandros Giagkos

  3. Aberystwyth University, Aberystwyth, United Kingdom

    Myra Wilson

  4. University of Southern Denmark, Odense, Denmark

    John Hallam

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Moore, J.M., McKinley, P.K. (2016). A Comparison of Multiobjective Algorithms in Evolving Quadrupedal Gaits. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43488-9_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43487-2

  • Online ISBN: 978-3-319-43488-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation