Evolution of Stochastic Bio-Networks Using Summed Rank Strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ross:2011:EoSBUSRS,

• title = "Evolution of Stochastic Bio-Networks Using Summed Rank Strategies",
• author = "Brian Ross",
• pages = "772--779",
• booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary Computation",
• year = "2011",
• editor = "Alice E. Smith",
• month = "5-8 " # jun,
• address = "New Orleans, USA",
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Biometrics, bioinformatics and biomedical applications",
• DOI = "doi:10.1109/CEC.2011.5949697",
• abstract = "Stochastic models defined in the stochastic pi-calculus are evolved using genetic programming. The interpretation of a stochastic model results in a set of time series behaviours. Each time series denotes changing quantities of components within the modelled system. The time series are described by their statistical features. This paper uses genetic programming to reverse engineer stochastic pi-calculus models. Given the statistical characteristics of the intended model behavior, genetic programming attempts to construct a model whose statistical features closely match those of the target process. The feature objectives comprising model behaviour are evaluated using a multi-objective strategy. A contribution of this research is that, rather than use conventional Pareto ranking, a summed rank scoring strategy is used instead. Summed rank scoring was originally derived for high-dimensional search spaces. This paper shows that it is likewise effective for evaluating stochastic models with low- to moderate-sized search spaces. Two models with oscillating behaviours were successfully evolved, and these results are superior to those obtained from earlier research attempts. Experiments on a larger-sized model were not successful. Reasons for its poor performance likely include inappropriate choices in feature selection, and too many selected features and channels contributing to an overly difficult search space.",
• notes = "CEC2011 sponsored by the IEEE Computational Intelligence Society, and previously sponsored by the EPS and the IET.",

}

Genetic Programming entries for Brian J Ross

Citations