genetic-programming | Symbolic regression solver , based on genetic programming | Machine Learning library
kandi X-RAY | genetic-programming Summary
kandi X-RAY | genetic-programming Summary
Each mathematical expression can be represented in form of syntax tree: ![Syntax Tree Example] Actually, it worth to keep in mind, that there exists infinite number of different syntax trees, which corresponds to semantically equivalent expressions. For example: ![Equivalent Syntax Trees] In practice, on of the most generic problems - is reconstruction of original function, having the information about its values in some specific points.
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- Main method for testing
- Get training data from a line
- Get variable list
- Reads functions
- Mutate the tree
- Optimizes the tree of the given syntax tree using iterations
- Creates a tree tree
- Mutates a function by changing the random value of a function
- The main method
- Add a listener to the engine s best fit of an iteration
- Example of testing
- Add an optimization listener to the engine
- Estimate the fitness of an expression
- Computes the derivative of an expression
- Evaluate the fitness function
- Evaluates the fitness for the given expression
- Creates a population of the chromosome with the given fitness function
- Main method
- Entry point for testing
- Clones this chromosome with another chromosome
- Main entry point for the program
genetic-programming Key Features
genetic-programming Examples and Code Snippets
Lines of Code : 39
License : No License
Copyfrom tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
@article{le2020scaling,
title={Scaling tree-based automated machine learning to biomedical big data with a feature set selector},
author={Le, Trang T and Fu, Weixuan and Moore, Jason H},
journal={Bioinformatics},
volume={36},
number={1},
from tpot import TPOTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
housing = load_boston()
X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target,
Community Discussions
Trending Discussions on genetic-programming
QUESTION
Multiple, perhaps most, language implementations that include a compiler at runtime neglect to garbage-collect discarded code (See, for example julia, where this leads to memory leaks in applications like genetic-programming)
My preliminary tests indicate that Chez Scheme does not leak memory here, but I would like to know with greater certainty, since I don't even know if f and g actually get compiled. (The old mantra: "Tests can only prove the presence of bugs, not their absence")
The test I tried: f and g call each other, and their definitions get replaced at runtime.
ANSWER
Answered 2020-Dec-24 at 07:31Given the importance of both procedures and garbage collection to Scheme, I would be surprised if Chez Scheme did not try to garbage collect any dynamically created objects. The R6RS Standard says [emphasis mine]:
All objects created in the course of a Scheme computation, including procedures and continuations, have unlimited extent. No Scheme object is ever destroyed. The reason that implementations of Scheme do not (usually!) run out of storage is that they are permitted to reclaim the storage occupied by an object if they can prove that the object cannot possibly matter to any future computation.
A procedure is an object, and any object may be garbage collected if the implementation can prove that the computation will not need it again. This is not a requirement, but that goes for any object, not just for procedures.
The Chez Scheme manual seems definitive, though (Chez Scheme Version 9 User's Guide, p. 82):
Since all Scheme objects, including code objects, can be relocated or even reclaimed by the garbage collector....
In the 1990s Kent Dybvig wrote a paper together with David Eby and Carl Bruggeman which may be of interest here, called Don’t Stop the BIBOP: Flexible and Efficient Storage Management for Dynamically Typed Languages, which describes the garbage collection strategy implemented in Chez Scheme. In the paper some time is spent discussing "code objects" and in particular how they are segregated and treated differently during the garbage collection process (since they may contain pointers to other objects).
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