Skip to main content
SpringerLink
Log in
Book cover

Genetic Programming Theory and Practice IV pp 113–130Cite as

Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations

  • Chapter

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

Fitness models are used to reduce evaluation frequency and cost. There are three fundamental challenges faced when using fitness models: (1) the model learning investment, (2) the model level of approximation, and (3) the lack of convergence to optima. We propose a coevolutionary algorithm to resolve these problems automatically during evolution. We discuss applications of this approach and measure its impact in symbolic regression. Results show coevolution yields significant improvement in performance over other algorithms and different fitness modeling approaches. Finally we apply coevolution to interactive evolution of pen stroke drawings where no true fitness function is known. These results demonstrate coevolution’s ability to infer a fitness landscape of a user’s preference while minimizing user interaction.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Albert, Laura A. and Goldberg, David E. (2002). Efficient discretization scheduling in multiple dimensions. In Langdon, W. B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A. C., Miller, J. F., Burke, E., and Jonoska, N., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 271–278, New York. Morgan Kaufmann Publishers.

    Google Scholar 

  • Augusto, D.A. and Barbosa, H.J.C. (2000). Symbolic regression via genetic programming. In VI Brazilian Symposium on Neural Networks (SBRN’00), volume 1.

    Google Scholar 

  • Bongard, J.C. and Lipson, H. (2004). Nonlinear system identification using co-evolution of models and tests. In IEEE Transactions on Evolutionary Computation.

    Google Scholar 

  • Bongard, Josh C. and Lipson, Hod (2005). ‘managed challenge’ alleviates disengagement in co-evolutionary system identification. In Beyer, Hans-Georg, O’Reilly, Una-May, Arnold, Dirk V., Banzhaf, Wolfgang, Blum, Christian, Bonabeau, Eric W., Cantu-Paz, Erick, Dasgupta, Dipankar, Deb, Kalyan-moy, Foster, James A., de Jong, Edwin D., Lipson, Hod, Llora, Xavier, Man-coridis, Spiros, Pelikan, Martin, Raidl, Guenther R., Soule, Terence, Tyrrell, Andy M., Watson, Jean-Paul, and Zitzler, Eckart, editors, GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, volume 1, pages 531–538, Washington DC, USA. ACM Press.

    Chapter  Google Scholar 

  • Chen, Jian-Hung, Goldberg, David E., Ho, Shinn-Ying, and Sastry, Kumara (2002). Fitness inheritance in multi-objective optimization. In Langdon, W. B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M. A., Schultz, A. C, Miller, J. F., Burke, E., and Jonoska, N., editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 319–326, New York. Morgan Kaufmann Publishers.

    Google Scholar 

  • Cliff, Dave and Miller, Geoffrey F. (1995). Tracking the red queen: Measurements of adaptive progress in co-evolutionary simulations. In Morán, F, Moreno, A., Merelo, J. J., and Chacón, P., editors, Proceedings of the Third European Conference on Artificial Life: Advances in Artificial Life, volume 929 of LNAI, pages 200–218, Berlin. Springer Verlag.

    Google Scholar 

  • Dolin, B., Bennett, F., and Rieffel, E. (2002). Co-evolving an effective fitness sample: Experiments in symbolic regression and distributed robot control. In Proceedings of the 2002 ACM Symposium on Applied Computing.

    Google Scholar 

  • Duffy, J. and Engle-Warnick, J. (2002). Using symbolic regression to infer strategies from experimental data. In Chen, S.H, editor, Evolutionary Computation in Economics and Finance. Physica-Verlag, New York.

    Google Scholar 

  • Eggermont, J. and van Hemert, J.I. (2001). Stepwise adaptation of weights for symbolic regression with genetic programming. In Proceedings of EuroGP.

    Google Scholar 

  • Ferreira, C. (2003). Function finding and the creation of numerical constants in gene experssion programming. In Advances in Soft Computing-Engineering Design and Manufacturing, pages 257–266. Springer-Verlag.

    Google Scholar 

  • Ficici, S.G. (2004). Solution Concepts in Co evolutionary Algorithms. PhD thesis, Computer Science Dept., Brandeis University.

    Google Scholar 

  • Ficici, S.G. and Pollack, J.B. (2001). Pareto optimality in coevolutionary learning. In Advances in Artificial Life, 6th European Conference (ECAL2001), pages 316–327.

    Google Scholar 

  • Hillis, W. Daniel (1992). Co-evolving parasites improve simulated evolution as an optimization procedure. In Langton, Christopher G., Taylor, Charles, Farmer, J. Doyne, and Rasmussen, Steen, editors, Artificial life II, volume 10 of Sante Fe Institute Studies in the Sciences of Complexity, pages 313–324. Addison-Wesley, Redwood City, Calif.

    Google Scholar 

  • Hoai, N. X., McKay, R. I., Essam, D., and Chau, R. (2002). Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: The comparative results. In Fogel, David B., El-Sharkawi, Mohamed A., Yao, Xin, Greenwood, Garry, Iba, Hitoshi, Marrow, Paul, and Shackleton, Mark, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 1326–1331. IEEE Press.

    Google Scholar 

  • Jin, Y. (Jan 2005). A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing-A fusion of Foundations, Methodologies and Applications, 9(1):3.

    Google Scholar 

  • Jin, Yaochu, Olhofer, Markus, and Sendhoff, Bernhard (2001). Managing approximate models in evolutionary aerodynamic design optimization. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 592–599, COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea. IEEE Press.

    Google Scholar 

  • Jin, Yaochu and Sendhoff, Bernhard (2004). Reducing fitness evaluations using clustering techniques and neural network ensembles. In Deb, Kalyanmoy, Poli, Riccardo, Banzhaf, Wolfgang, Beyer, Hans-Georg, Burke, Edmund, Darwen, Paul, Dasgupta, Dipankar, Floreano, Dario, Foster, James, Harman, Mark, Holland, Owen, Lanzi, Pier Luca, Spector, Lee, Tettamanzi, Andrea, Thierens, Dirk, and Tyrrell, Andy, editors, Genetic and Evolutionary Computation-GECCO-2004, Part I, volume 3102 of Lecture Notes in Computer Science, pages 688–699, Seattle, WA, USA. Springer-Verlag.

    Google Scholar 

  • Jong, Edwin D. De and Pollack, Jordan B. (2004). Ideal Evaluation from Co-evolution. Evolutionary Computation, 12(2): 159–192.

    Article  Google Scholar 

  • Keijzer, M. (2003). Improving symbolic regression with interval arithmetic and linear scaling. In Sixth European Conference on Genetic Programming, pages 70–82, Essex, UK. Springer.

    Google Scholar 

  • Koza, J. R. (1992). Genetic Programming: On the programming of computers by means of natural selection. Bradford Books, Cambridge, MA.

    MATH  Google Scholar 

  • Luke, Sean and Wiegand, R. Paul (2002). When coevolutionary algorithms exhibit evolutionary dynamics. In Barry, Alwyn M., editor, GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 236–241, New York. AAAI.

    Google Scholar 

  • Ong, Y., Nair, P., and Keane, A. (2003). evolutionary optimization of computationally expensive problems via surrogate modeling. AJAA J., 41(4):678–696.

    Google Scholar 

  • Potter, Mitchell A. and De Jong, Kenneth A. (2000). Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1): 1–29.

    Article  Google Scholar 

  • Rosin, Christopher D. and Belew, Richard K. (1996). New methods for competitive coevolution. Technical Report CS96-491, Cognitive Computer Science Research Group, University of California, San Diego, CA. to appear in Evolutionary Computation 5:1.

    Google Scholar 

  • Soule, Terence and Heckendorn, Robert B. (2001). Function sets in genetic programming. In Spector, Lee, Goodman, Erik D., Wu, Annie, Langdon, W. B., Voigt, Hans-Michael, Gen, Mitsuo, Sen, Sandip, Dorigo, Marco, Pezeshk, Shahram, Garzon, Max H., and Burke, Edmund, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), page 190, San Francisco, California, USA. Morgan Kaufmann.

    Google Scholar 

  • Stanley, K. O. and Miikkulainen, R. (2004). Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 21:63–100.

    Google Scholar 

  • Watson, Richard A. and Pollack, Jordan B. (2001). Coevolutionary dynamics in a minimal substrate. In Spector, Lee, Goodman, Erik D., Wu, Annie, Langdon, W. B., Voigt, Hans-Michael, Gen, Mitsuo, Sen, Sandip, Dorigo, Marco, Pezeshk, Shahram, Garzon, Max H., and Burke, Edmund, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 702–709, San Francisco, California, USA. Morgan Kaufmann.

    Google Scholar 

  • Yang, Dekun and Flockton, Stuart J. (1995). Evolutionary algorithms with a coarse-to-fine function smoothing.

    Google Scholar 

  • Zykov, V., Bongard, J., and Lipson, H. (2005). Co-evolutionary variance guides physical experimentation in evolutionary system indentification. In The 2005 NASA/DoD Conference on Evolvable Hardware.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science, Cornell University, USA

    Michael D. Schmidt

  2. Mechanical & Aerospace Engineering, Cornell University, USA

    Hod Lipson

Authors
  1. Michael D. Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hod Lipson
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, USA

    Rick Riolo

  2. University of Idaho, USA

    Terence Soule

  3. Genetics Squared, Inc., USA

    Bill Worzel

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Schmidt, M.D., Lipson, H. (2007). Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-49650-4_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-33375-5

  • Online ISBN: 978-0-387-49650-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation