Advertisement

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

Chapter
Part of the Genetic and Evolutionary Computation book series (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.

Keywords

Symbolic regression Forecasting DataModeler Extrapolation Prediction Simulation-based optimization 

References

  1. 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):S1CrossRefGoogle Scholar
  2. Chao D, Halloran M, Obenchain V, Longini I (2010) FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol 6(1):e1000,656MathSciNetCrossRefGoogle Scholar
  3. Crombecq K (2011) Surrogate modelling of computer experiments with sequential experimental design. Ph.D. thesis, University of Antwerp, AntwerpGoogle Scholar
  4. 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 80–84Google Scholar
  5. 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 731–742Google Scholar
  6. Evolved Analytics LLC (2011) DataModeler Release 8.0 Documentation. Evolved Analytics LLC - www.evolved-analytics.com
  7. 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):209–214CrossRefGoogle Scholar
  8. Ferguson N, Cummings D, Fraser C, Cajka J, Cooley P, Burke D (2006) Strategies for mitigating an influenza pandemic. Nature 442(7101):448–452CrossRefGoogle Scholar
  9. Germann T, Kadau K, Longini Jr I, Macken C (2006) Mitigation strategies for pandemic influenza in the United States. PNAS 103(15):5935–5940CrossRefGoogle Scholar
  10. 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):4639–4644CrossRefGoogle Scholar
  11. Husslage B, Rennen G, Van Dam ER, Den Hertog D (2006) Space-filling Latin hypercube designs for computer experiments. Tilburg UniversityzbMATHGoogle Scholar
  12. 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
  13. 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
  14. 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
  15. 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 32:237–253CrossRefGoogle Scholar
  16. 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):1540–1548CrossRefGoogle Scholar
  17. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, New YorkCrossRefzbMATHGoogle Scholar
  18. 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
  19. 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
  20. 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 623–630Google Scholar
  21. Trustable symbolic regression models: using ensembles, interval arithmetic and pareto fronts to develop robust and trust-aware modelsGoogle Scholar
  22. 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
  23. 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

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematics - Computer SciencesUniversity of AntwerpAntwerpBelgium
  2. 2.Evolved Analytics Europe BVBABeerseBelgium
  3. 3.Kordon Consulting LLCFort LauderdaleUSA
  4. 4.Faculty of Medicine and Health SciencesUniversity of AntwerpAntwerpBelgium
  5. 5.Evolved Analytics LLCMidlandUSA

Personalised recommendations