Dear all,
Please share your view and relevant materials for symbolic regression in optimization methods.
Thanks
In normal regression you try to do
curve fitting by modifying numerical
values of usually a scalar, a vector or
matrix in order to minimize e.g. a sum
of least squares. The result of such an
optimization is a tuple of reals.
In symbolic regression the formula itself
is being rearranged. The result is
a formula along with numerical parameters.
How is it achieved? It can be done with
a genetic algorithm. A formula is a sequence
of objects of class real number, '+', ','
..., 'sin', 'cos', 'exp'. Parentheses
not needed since you're working either
with trees or Polish / Reverse Polish
notation.
The tricky part is that during genetic
operations a so-called well formed
sequence must be generated, but that
is feasible by avoiding steps that violate
this principle. For example, you can use
Polish notation. A binary function results
in one value and expects two values right
of it. Easy to check recursively or with
a stack.
The fitness of such a formula can be
determined by evaluating it at certain
nodes and summing up the squares of
the error. Actually it's the principle
of an interpreter.
Regards,
Joachim
In normal regression you try to do
curve fitting by modifying numerical
values of usually a scalar, a vector or
matrix in order to minimize e.g. a sum
of least squares. The result of such an
optimization is a tuple of reals.
In symbolic regression the formula itself
is being rearranged. The result is
a formula along with numerical parameters.
How is it achieved? It can be done with
a genetic algorithm. A formula is a sequence
of objects of class real number, '+', ','
..., 'sin', 'cos', 'exp'. Parentheses
not needed since you're working either
with trees or Polish / Reverse Polish
notation.
The tricky part is that during genetic
operations a so-called well formed
sequence must be generated, but that
is feasible by avoiding steps that violate
this principle. For example, you can use
Polish notation. A binary function results
in one value and expects two values right
of it. Easy to check recursively or with
a stack.
The fitness of such a formula can be
determined by evaluating it at certain
nodes and summing up the squares of
the error. Actually it's the principle
of an interpreter.
Regards,
Joachim
Dear Pimiskern,
Thank you very much for detailed description on symbolic regression. I would be glad if you please share a Matlab code in which symbolic regression is implemented. Form theory of symbolic regression, it is very difficult for me to understand it.
Thanks
with
Warm regards
Dear Ryser-Welch,
It is difficult for me to identify Prof. Nuno Lourenco. I request please share any one research paper for the same.
Thanks
with
Warm regards
Unfortunately I have no code in Matlab.
Here is a paper by Nuno Lorenceo et al.
Studying the Properties of Structured Grammatical Evolution
ECOS-CISUC Technical Report TR2015/01
Nuno Lourenco, Francisco B. Pereira, Ernesto Costa
https://www.cisuc.uc.pt/ckfinder/userfiles/files/TR%202015-01.pdf
An introduction:
http://creativemachines.cornell.edu/sites/default/files/Science09_Schmidt.pdf
Regards,
Joachim
Hello Ghanshyam Tejani
The following software are available for implementing symbolic regression:
GPTIPS software: is a free, open source, MATLAB based symbolic regression software
Eureqa software: implements symbolic regression
DataMelt software: implements symbolic regression in Java and Python
RGP software: implements symbolic regression in R
You don't need to write your own code to use these software.
Eureka is freely downloadable from (M. Schmidt Eureqa web page) http://creativemachines.cornell.edu/eureqa
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics
- Regression