CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python
A stochastic numerical optimization algorithm for difficult
(non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization
problems in continuous search spaces, implemented in Python.
- Developed at devel:languages:python:numeric
- Sources inherited from project openSUSE:Factory
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Source Files
| Filename | Size | Changed |
|---|---|---|
| cma-3.2.2.tar.gz | 0000538458 526 KB | |
| python-cma.changes | 0000001262 1.23 KB | |
| python-cma.spec | 0000002092 2.04 KB |
Revision 3 (latest revision is 4)
Dominique Leuenberger (dimstar_suse)
accepted
request 1055966
from
Dirk Mueller (dirkmueller)
(revision 3)
- update to r3.2.2:
* Smallish fixes and improvements and a constraints use case notebook.
* fixes plot of principal axes which were shown squared by mistake
- update to r3.2.0:
* constraints handling via a dynamic unconstrained function instantiated
from cma.ConstrainedFitnessAL.
* many small improvements and fixes
- update to r3.1.0:
* fix return value of fmin_con and make it more usable, added attribute
best_feasible
* polish evolution_strategy.py
* fix a few smallish bugs
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