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Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes

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Genetic Programming Theory and Practice VII

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

Abstract

Genetic programming is used to automatically construct stochastic processes written in the stochastic π-calculus. Grammar-guided genetic programming constrains search to useful process algebra structures. The time-series behaviour of a target process is denoted with a suitable selection of statistical feature tests. Feature tests can permit complex process behaviours to be effectively evaluated. However, they must be selected with care, in order to accurately characterize the desired process behaviour. Multi-objective evaluation is shown to be appropriate for this application, since it permits heterogeneous statistical feature tests to reside as independent objectives. Multiple undominated solutions can be saved and evaluated after a run, for determination of those that are most appropriate. Since there can be a vast number of candidate solutions, however, strategies for filtering and analyzing this set are required.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Brock University, St. Catharines, Ontario, L2S 3A1, Canada

    Brian Ross & Janine Imada

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  1. Brian Ross
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  2. Janine Imada
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, U.S.A.

    Rick Riolo

  2. Computer Science &, Massachusetts Institute of Technology, Vassar St. 32, Cambridge, 02139, U.S.A.

    Una-May O'Reilly

  3. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Canada

    Trent McConaghy

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Ross, B., Imada, J. (2010). Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_10

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  • DOI: https://doi.org/10.1007/978-1-4419-1626-6_10

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1653-2

  • Online ISBN: 978-1-4419-1626-6

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