%0 Conference Paper
%1 iba:2002:gecco
%A Iba, Hitoshi
%A Sakamoto, Erina
%B GECCO 2002: Proceedings of the Genetic and
                 Evolutionary Computation Conference
%C New York
%D 2002
%E Langdon, W. B.
%E Cantú-Paz, E.
%E Mathias, K.
%E Roy, R.
%E Davis, D.
%E Poli, R.
%E Balakrishnan, K.
%E Honavar, V.
%E Rudolph, G.
%E Wegener, J.
%E Bull, L.
%E Potter, M. A.
%E Schultz, A. C.
%E Miller, J. F.
%E Burke, E.
%E Jonoska, N.
%I Morgan Kaufmann Publishers
%K E-cell, Lotka-Volterra S-systems algorithms, bioinformatics, differential equation, genetic genome informatics, model, programming,
%P 788--795
%T Inference Of Differential Equation Models By Genetic
                 Programming
%U http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf
%X 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.
%@ 1-55860-878-8