Evolving Bio-PEPA process algebra models using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "David Marco and Carron Shankland and David Cairns",
  title =        "Evolving Bio-PEPA process algebra models using genetic
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "177--184",
  keywords =     "genetic algorithms, genetic programming,
                 bioinformatics, computational, systems and synthetic
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330189",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper presents initial results of applying a
                 Genetic Programming (GP) approach to the evolution of
                 process algebra models defined in Bio-PEPA. An
                 incomplete model of a system is provided together with
                 target behaviour. GP is then used to evolve new
                 definitions that complete the model while ensuring a
                 good fit to target data. Our results show that a set of
                 effective models can be developed with this approach
                 that can either be used directly or further refined
                 using a modeller's domain knowledge. Such an approach
                 can greatly reduce the time taken to develop new
                 models, enabling a modeller to focus on the subtler
                 modelling aspects of the problem domain. Although the
                 work presented here concerns the modelling of
                 biological systems, the approach is generally
                 applicable to systems for which appropriate target
                 behaviour can be captured and that can be formalised as
                 a set of communicating processes.",
  notes =        "Also known as \cite{2330189} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for David Marco Carron Shankland David Cairns