Using Multi-objective Genetic Programming to Synthesize Stochastic Processes

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Ross:2009:GPTP,

• author = "Brian J. Ross and Janine Imada",
• title = "Using Multi-objective Genetic Programming to Synthesize Stochastic Processes",
• booktitle = "Genetic Programming Theory and Practice {VII}",
• year = "2009",
• editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy",
• series = "Genetic and Evolutionary Computation",
• address = "Ann Arbor",
• month = "14-16 " # may,
• publisher = "Springer",
• chapter = "10",
• pages = "159--175",
• keywords = "genetic algorithms, genetic programming, stochastic processes, process algebra, time-series feature tests, multi-objective gp, MOGP",
• isbn13 = "978-1-4419-1653-2",
• DOI = "doi:10.1007/978-1-4419-1626-6_10",
• abstract = "Genetic programming is used to automatically construct stochastic processes written in the stochastic pi-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 characterise 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 analysing this set are required.",
• notes = "part of \cite{Riolo:2009:GPTP}",

}

Genetic Programming entries for Brian J Ross Janine H Imada

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