Skip to main content
SpringerLink
Log in

Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion

  • Chapter
  • First Online:
Book cover Genetic Programming Theory and Practice XIV

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

  • 524 Accesses

Abstract

We investigate the application of a version of Genetic Programming with grammars, called Grammatical Evolution, and a multi-population competitive coevolutionary algorithm for anticipating tax evasion in the domain of U.S. Partnership tax regulations. A problem in tax auditing is that as soon as one evasion scheme is detected a new, slightly mutated, variant of that scheme appears. Multi-population competitive coevolutionary algorithms are disposed to explore adversarial problems, such as the arms-race between tax evader and auditor. In addition, we use Genetic Programming and grammars to represent and search the transactions of tax evaders and tax audit policies. Grammars are helpful for representing and biasing the search space. The feasibility of the method is studied with an example of adversarial coevolution in tax evasion. We study the dynamics and the solutions of the competing populations in this scenario, and note that we are able to replicate some of the expected behavior.

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
Hardcover Book
USD 54.99
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

References

  1. Adamu, K., Phelps, S.: Coevolutionary grammatical evolution for building trading algorithms. In: Electrical Engineering and Applied Computing, pp. 311–322. Springer, Dordrecht (2011)

    Chapter  Google Scholar 

  2. Alfonseca, M., Gil, S.: Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution. Complexity 20(3), 66–83 (2015)

    Article  Google Scholar 

  3. Azad, R.M.A., Ryan, C.: An examination of simultaneous evolution of grammars and solutions. In: Genetic Programming Theory and Practice III, pp. 141–158. Springer, New York (2006)

    Google Scholar 

  4. Byrne, J., Cardiff, P., Brabazon, A., et al.: Evolving parametric aircraft models for design exploration and optimisation. Neurocomputing 142, 39–47 (2014)

    Article  Google Scholar 

  5. Chandler, S.J.: A ‘genetically modified’ liability insurance contract. University of Houston Law Center No. 2007-W-01 (2007)

    Google Scholar 

  6. Chennupati, G., Azad, R.M.A., Ryan, C.: Automatic evolution of parallel sorting programs on multi-cores. In: Applications of Evolutionary Computation, pp. 706–717. Springer, Cham (2015)

    Google Scholar 

  7. Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments, vol. 194. Springer, Berlin (2009)

    Google Scholar 

  8. Gabrielsson, P., Johansson, U., Konig, R.: Co-evolving online high-frequency trading strategies using grammatical evolution. In: IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104, pp. 473–480. IEEE, Piscataway (2014)

    Google Scholar 

  9. GAO: Gao-14-453 (2013). http://www.youtube.com/watch?v=O8VDUStvxMY

  10. GAO (2014). http://www.gao.gov/assets/670/663185.pdf

  11. Haddadi, F., Nur Zincir-Heywood, A.: Botnet detection system analysis on the effect of botnet evolution and feature representation. In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 893–900. ACM, New York (2015)

    Google Scholar 

  12. Harper, R.: Evolving robocode tanks for evo robocode. Genet. Program Evolvable Mach. 15(4), 403–431 (2014)

    Article  Google Scholar 

  13. Heywood, M.I.: Evolutionary model building under streaming data for classification tasks: opportunities and challenges. Genet. Program Evolvable Mach. 16(3), 283–326 (2015)

    Article  MathSciNet  Google Scholar 

  14. Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, Orlando (1999)

    Google Scholar 

  15. Le Goues, C., Nguyen-Tuong, A., Chen, H., Davidson, J.W., Forrest, S., Hiser, J.D., Knight, J.C., Van Gundy, M.: Moving target defenses in the helix self-regenerative architecture. In: Moving Target Defense II, pp. 117–149. Springer, Cham (2013)

    Google Scholar 

  16. McIntyre, A.R., Heywood, M.I.: Multi-objective competitive coevolution for efficient GP classifier problem decomposition. In: IEEE International Conference on Systems, Man and Cybernetics, 2007, ISIC, pp. 1930–1937. IEEE, Piscataway (2007)

    Google Scholar 

  17. McIntyre, A.R., Heywood, M.I.: Cooperative problem decomposition in Pareto competitive classifier models of coevolution. In: Genetic Programming, pp. 289–300. Springer, Berlin (2008)

    Google Scholar 

  18. O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language, vol. 4. Springer, New York (2003)

    Book  Google Scholar 

  19. Popovici, E., Bucci, A., Paul Wiegand, R., De Jong, E.D.: Coevolutionary principles. In: Handbook of Natural Computing, pp. 987–1033. Springer, Berlin (2012)

    Chapter  Google Scholar 

  20. Rosen, J.: Computer Aided Tax Avoidance Policy Analysis. Massachusetts Institute of Technology, Cambridge (2015)

    Google Scholar 

  21. Rush, G., Tauritz, D.R., Kent, A.D.: Coevolutionary agent-based network defense lightweight event system (candles). In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 859–866. ACM, New York (2015)

    Google Scholar 

  22. Sabar, N.R., Ayob, M., Kendall, G., Qu, R.: Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Trans. Evol. Comput. 17(6), 840–861 (2013)

    Article  Google Scholar 

  23. Shaker, N., Nicolau, M., Yannakakis, G.N., Togelius, J., Neill, M.O.: Evolving levels for super mario bros using grammatical evolution. In: IEEE Conference on Computational Intelligence and Games (CIG), 2012, pp. 304–311. IEEE, Piscataway (2012)

    Google Scholar 

  24. Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res. 21, 63–100 (2004)

    Article  Google Scholar 

  25. Tauritz, D.R. et al.: A no-free-lunch framework for coevolution. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 371–378. ACM, New York (2008)

    Google Scholar 

  26. Thorhauer, A., Rothlauf, F.: On the locality of standard search operators in grammatical evolution. In: Parallel Problem Solving from Nature–PPSN XIII, pp. 465–475. Springer, Cham (2014)

    Google Scholar 

  27. Whigham, P.A., Dick, G., Maclaurin, J., Owen, C.A.: Examining the best of both worlds of grammatical evolution. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1111–1118. ACM, New York (2015)

    Google Scholar 

  28. Winterrose, M.L., Carter, K.M.: Strategic evolution of adversaries against temporal platform diversity active cyber defenses. In: Proceedings of the 2014 Symposium on Agent Directed Simulation, p. 9. Society for Computer Simulation International, San Diego (2014)

    Google Scholar 

  29. Wright, D. Jr.: Financial alchemy: How tax shelter promoters use financial products to bedevil the IRS (and how the IRS helps them). Ariz. State Law J. 45, 611–675 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIT CSAIL, Cambridge, MA, USA

    Erik Hemberg, Jacob Rosen & Una-May O’Reilly

Authors
  1. Erik Hemberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacob Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Una-May O’Reilly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Hemberg .

Editor information

Editors and Affiliations

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

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

  3. Colorado State University, Fort Collins, CO, USA

    Brian Goldman

  4. Ann Arbor, MI, USA

    Bill Tozier

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hemberg, E., Rosen, J., O’Reilly, UM. (2018). Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds) Genetic Programming Theory and Practice XIV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-97088-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97088-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97087-5

  • Online ISBN: 978-3-319-97088-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation