Evolving Bio-PEPA process algebra models using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Marco:2012:GECCO,

• author = "David Marco and Carron Shankland and David Cairns",
• title = "Evolving Bio-PEPA process algebra models using genetic programming",
• booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
• 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 biology",
• 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

Citations