A new implementation of OpenMLC (https://github.com/MachineLearningControl/OpenMLC-Matlab) for Matlab.
Faster: Individuals are hashed and thus all comparisons are done in a breath, only identical hashes are checked.
More information: individuals have now their own class and keep a small database of their history, including all past evaluations and evaluation time.
More options: Leaf probability, 4 mutation types, Different population sizes, subpopulations, cascading.
Slight changes for the scripts. If you have evaluation scripts for OpenMLC, please note that individuals are a bit different: idv.value is now the LISP expression (and they have the root operator even if only one branch is used), while a MATLAB interpretable expression can be found at idv.formal.
More bugs: More code means more bugs. Please report at https://github.com/tduriez/OpenMLC-Matlab-2/issues.
Machine Learning Control is a framework develloped in order to control experimental complex systems without the need for modeling. It is based on the direct regression of a control law which minimizes a cost functionnal.
You can find more information about MLC in the MLC Book or check this page for related publications.
While I will fix bugs under finite time, I will only be including new features that serves my work or through pull request.
Have fun, T.
Copyright (C) 2015-2019 Thomas Duriez.
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