Skip to main content
SpringerLink
Log in

Prime-Time: Symbolic Regression Takes Its Place in the Real World

  • Chapter
  • First Online:
Genetic Programming Theory and Practice XIII

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

Abstract

In this chapter we review a number of real-world applications where symbolic regression was used recently and with great success. Industrial scale symbolic regression armed with the power to select right variables and variable combinations, build robust trustable predictions and guide experimentation has undoubtedly earned its place in industrial process optimization, business forecasting, product design and now complex systems modeling and policy making.

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 EPUB and 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
Hardcover Book
USD 109.99
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

Institutional subscriptions

Notes

  1. 1.

    Attack rate is defined as a ratio of the new cases in the population at risk to the total size of the population at risk.

References

  • Andradóttir S, Chiu W, Goldsman D, Lee M, Tsui K, Sander B, Fisman D, Nizam A (2011) Reactive strategies for containing developing outbreaks of pandemic influenza. BMC Public Health 11(Suppl 1):S1

    Article  Google Scholar 

  • Chao D, Halloran M, Obenchain V, Longini I (2010) FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol 6(1):e1000,656

    Article  MathSciNet  Google Scholar 

  • Crombecq K (2011) Surrogate modelling of computer experiments with sequential experimental design. Ph.D. thesis, University of Antwerp, Antwerp

    Google Scholar 

  • Crombecq K, Dhaene T (2010) Generating sequential space-filling designs using genetic algorithms and monte carlo methods. In: Simulated evolution and learning. Lecture notes in computer science, vol 6457. Springer, Berlin, pp 80–84

    Google Scholar 

  • Crombecq K, De Tommasi L, Gorissen D, Dhaene T (2009) A novel sequential design strategy for global surrogate modeling. In: Winter simulation conference, Austin, Texas, WSC ’09, pp 731–742

    Google Scholar 

  • Evolved Analytics LLC (2011) DataModeler Release 8.0 Documentation. Evolved Analytics LLC - www.evolved-analytics.com

  • Ferguson N, Cummings D, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke D (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437(7056):209–214

    Article  Google Scholar 

  • Ferguson N, Cummings D, Fraser C, Cajka J, Cooley P, Burke D (2006) Strategies for mitigating an influenza pandemic. Nature 442(7101):448–452

    Article  Google Scholar 

  • Germann T, Kadau K, Longini Jr I, Macken C (2006) Mitigation strategies for pandemic influenza in the United States. PNAS 103(15):5935–5940

    Article  Google Scholar 

  • Halloran M, Ferguson N, Eubank S, Longini I, Cummings D, Lewis B, Xu S, Fraser C, Vullikanti A, Germann T, et al (2008) Modeling targeted layered containment of an influenza pandemic in the United States. PNAS 105(12):4639–4644

    Article  Google Scholar 

  • Husslage B, Rennen G, Van Dam ER, Den Hertog D (2006) Space-filling Latin hypercube designs for computer experiments. Tilburg University

    MATH  Google Scholar 

  • Kordon AK, Smits GF (2001) Soft sensor development using genetic programming. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the genetic and evolutionary computation conference (GECCO-2001), Morgan Kaufmann, San Francisco, California, pp 1346–1351. http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf

  • Kordon AK (2012) Applying intelligent systems in industry: a realistic overview. In proceedings of the 6th IEEE international conference intelligent systems. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6335108

  • Kordon AK (2014) Applying genetic programming in business forecasting. Genetic programming theory and practice XI. http://link.springer.com/chapter/10.1007/978-1-4939-0375-7_6

    Google Scholar 

  • Ma J, Ackerman E, Yang J (1993) Parameter sensitivity of a model or viral epidemics simulated with Monte Carlo techniques. I. illness attack rates. Int J Biomed Comput 32:237–253

    Article  Google Scholar 

  • Piedra P, Gaglani M, Kozinetz C, Herschler G, Riggs M, Griffith M, Fewlass C, Watts M, Hessel C, Cordova J, et al (2005) Herd immunity in adults against influenza-related illnesses with use of the trivalent-live attenuated influenza vaccine (CAIV-T) in children. Vaccine 23(13):1540–1548

    Article  Google Scholar 

  • Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, New York

    Book  MATH  Google Scholar 

  • Smits G, Kotanchek M (2004) Pareto-front exploitation in symbolic regression, Chap. 17 In: O’Reilly UM, Yu T, Riolo RL, Worzel B (eds) Genetic programming theory and practice II. Springer, Ann Arbor, pp 283–299. doi:10.1007/0-387-23254-0_17

  • Smits G, Vladislavleva E (2008) Trustable symbolic regression models: using ensembles interval arithmetic and pareto fronts to develop robust and trust aware models. In: Dow Benelux BV, Terneuzen (eds) Tilburg University, Tilburg, the Netherlands. Evolved-Analytics, LLC, Midland, MI, USA http://link.springer.com/chapter/10.1007{%}2F978-0-387-76308-8_12

  • Stijven S, Minnebo W, Vladislavleva K (2011) Separating the wheat from the chaff: on feature selection and feature importance in regression random forests and symbolic regression. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation, Dublin, GECCO ’11, pp 623–630

    Google Scholar 

  • Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware models

    Google Scholar 

  • Vladislavleva E, Smits G, Kotanchek M (2008) Better solutions faster: soft evolution of robust regression models in pareto genetic programming. In: Dow Benelux BV, Terneuzen (eds) Tilburg University, Tilburg, the Netherlands. Evolved-Analytics, LLC, Midland, MI, USA http://link.springer.com/chapter/10.1007%2F978-0-387-76308-8_2

  • Willem L, Stijven S, Vladislavleva E, Broeckhove J, Beutels P, Hens N (2014) Active learning to understand infectious disease models and improve policy making. PLoS Comput Biol 10(4). doi:10.1371/journal.pcbi.1003563. http://dx.doi.org/10.1371/journal.pcbi.1003563

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics - Computer Sciences, University of Antwerp, Antwerp, Belgium

    Sean Stijven

  2. Evolved Analytics Europe BVBA, Beerse, Belgium

    Ekaterina Vladislavleva

  3. Kordon Consulting LLC, Fort Lauderdale, FL, USA

    Arthur Kordon

  4. Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium

    Lander Willem

  5. Evolved Analytics LLC, Midland, MI, USA

    Mark E. Kotanchek

Authors
  1. Sean Stijven
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ekaterina Vladislavleva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arthur Kordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lander Willem
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mark E. Kotanchek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sean Stijven .

Editor information

Editors and Affiliations

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

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, Michigan, USA

    W.P. Worzel

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

  4. Evolved Analytics LLC, Midland, Michigan, USA

    Arthur Kordon

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Stijven, S., Vladislavleva, E., Kordon, A., Willem, L., Kotanchek, M.E. (2016). Prime-Time: Symbolic Regression Takes Its Place in the Real World. In: Riolo, R., Worzel, W., Kotanchek, M., Kordon, A. (eds) Genetic Programming Theory and Practice XIII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-34223-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-34223-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34221-4

  • Online ISBN: 978-3-319-34223-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation