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.
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