- author = "Erik Hemberg and Jacob Rosen and Una-May O'Reilly",
- title = "Investigating Multi population Competetive Coevolution for Anticipating of Tax Evasion",
- booktitle = "Genetic Programming Theory and Practice XIV",
- year = "2016",
- editor = "Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier",
- pages = "35--51",
- address = "Ann Arbor, USA",
- month = "19-21 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-319-97087-5",
-
URL = "
https://www.springer.com/us/book/9783319970875",
-
DOI = "
doi:10.1007/978-3-319-97088-2_3",
- 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 an evasion scheme is detected a new, slightly mutated, variant of the scheme appears. Multi population competitive coevolutionary algorithms are disposed to explore adversarial problems, such as the arms-race between tax evader and auditor. Furthermore, 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 explored 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 behaviour.",
-
notes = "
Part of \cite{Tozier:2016:GPTP} published after the workshop",
