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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamu, K., Phelps, S.: Coevolutionary grammatical evolution for building trading algorithms. In: Electrical Engineering and Applied Computing, pp. 311–322. Springer, Dordrecht (2011)
Alfonseca, M., Gil, S.: Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution. Complexity 20(3), 66–83 (2015)
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)
Byrne, J., Cardiff, P., Brabazon, A., et al.: Evolving parametric aircraft models for design exploration and optimisation. Neurocomputing 142, 39–47 (2014)
Chandler, S.J.: A ‘genetically modified’ liability insurance contract. University of Houston Law Center No. 2007-W-01 (2007)
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)
Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments, vol. 194. Springer, Berlin (2009)
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)
GAO: Gao-14-453 (2013). http://www.youtube.com/watch?v=O8VDUStvxMY
GAO (2014). http://www.gao.gov/assets/670/663185.pdf
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)
Harper, R.: Evolving robocode tanks for evo robocode. Genet. Program Evolvable Mach. 15(4), 403–431 (2014)
Heywood, M.I.: Evolutionary model building under streaming data for classification tasks: opportunities and challenges. Genet. Program Evolvable Mach. 16(3), 283–326 (2015)
Iba, H.: Bagging, boosting, and bloating in genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, Orlando (1999)
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)
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)
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)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language, vol. 4. Springer, New York (2003)
Popovici, E., Bucci, A., Paul Wiegand, R., De Jong, E.D.: Coevolutionary principles. In: Handbook of Natural Computing, pp. 987–1033. Springer, Berlin (2012)
Rosen, J.: Computer Aided Tax Avoidance Policy Analysis. Massachusetts Institute of Technology, Cambridge (2015)
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)
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)
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)
Stanley, K.O., Miikkulainen, R.: Competitive coevolution through evolutionary complexification. J. Artif. Intell. Res. 21, 63–100 (2004)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this chapter
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)