Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{iba:2002:gecco, author = "Hitoshi Iba and Erina Sakamoto", title = "Inference Of Differential Equation Models By Genetic Programming", booktitle = "GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference", editor = "W. B. Langdon and E. Cant{\'u}-Paz and K. Mathias and R. Roy and D. Davis and R. Poli and K. Balakrishnan and V. Honavar and G. Rudolph and J. Wegener and L. Bull and M. A. Potter and A. C. Schultz and J. F. Miller and E. Burke and N. Jonoska", year = "2002", pages = "788--795", address = "New York", publisher_address = "San Francisco, CA 94104, USA", month = "9-13 " # jul, publisher = "Morgan Kaufmann Publishers", keywords = "genetic algorithms, genetic programming, bioinformatics, differential equation, E-cell, genome informatics, Lotka-Volterra model, S-systems", ISBN = "1-55860-878-8", URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP042.ps", URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP042.pdf", URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf", abstract = "An evolutionary method for identifying a causal model from the observed time series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is well known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs.", notes = "GECCO-2002. A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)", }

Genetic Programming entries for Hitoshi Iba Erina Sakamoto