Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Keywords
Repository
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Basic Info
Statistics
- Stars: 66
- Watchers: 3
- Forks: 15
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
A TensorFlow 2 implementation.
What is CMA-ES?
The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex black-box optimisation problems in continuous domain. It is considered as state-of-the-art in evolutionary computation and has been adopted as one of the standard tools for continuous optimisation in many (probably hundreds of) research labs and industrial environments around the world.
Installation
The package is available on PyPI and can be installed with pip:
sh
pip install cma-es
Alternatively, cma-es can also be installed from conda-forge:
sh
conda install -c conda-forge cma-es
Example Usage
1. Define the fitness function
CMA attempts to minimize a user-defined fitness function.
Function signature:
```
Args:
x: tf.Tensor of shape (M, N)
Returns: Fitness evaluations: tf.Tensor of shape (M,) ```
Where M is the number of solutions to evaluate and N is the dimension of a single solution.
python
def fitness_fn(x):
"""
Six-Hump Camel Function
https://www.sfu.ca/~ssurjano/camel6.html
"""
return (
(4 - 2.1 * x[:,0]**2 + x[:,0]**4 / 3) * x[:,0]**2 +
x[:,0] * x[:,1] +
(-4 + 4 * x[:,1]**2) * x[:,1]**2
)
2. Configure CMA-ES
```python from cma import CMA
cma = CMA( initialsolution=[1.5, -0.4], initialstepsize=1.0, fitnessfunction=fitness_fn, ) ```
The initial solution and initial step size (i.e. initial standard deviation of the search distribution) are problem specific.
The population size is automatically set by default, but it can be overidden by specifying the parameter population_size.
For bounded constraint optimization problems, the parameter enforce_bounds can be set, e.g. enforce_bounds=[[-2, 2], [-1, 1]] for a 2D function.
3. Run the optimizer
The search method runs until the maximum number of generation is reached or until one of the early termination criteria is met. By default, the maximum number of generations is 500.
python
best_solution, best_fitness = cma.search()
The notebook Example 1 - Six Hump Camel Function goes into more details, including ways to plot the optimization path such as in the figure below.
Logging
A user-defined callback function can be specified to inspect variables during the search.
It is mainly intended for logging purpose, e.g:
```python max_epochs = 500
def loggingfunction(cma, logger): if cma.generation % 10 == 0: fitness = cma.bestfitness() logger.info(f'Generation {cma.generation} - fitness {fitness}')
if cma.termination_criterion_met or cma.generation == max_epochs:
sol = cma.best_solution()
fitness = cma.best_fitness()
logger.info(f'Final solution at gen {cma.generation}: {sol} (fitness: {fitness})')
cma = CMA( initialsolution=[1.5, -0.4], initialstepsize=1.0, fitnessfunction=fitnessfn, callbackfunction=loggingfunction, ) cma.search(maxepochs) ```
Check out an example logging progress to TensorBoard: tensorboard_example.py
Running on GPU
By virtue of using TensorFlow, CMA should make use of available GPUs without any code change.
If you run into issues, check the official TF documentation.
More examples
- Jupyter notebooks with examples are available:
- Unit tests provide a few more examples:
cma/core_test.py
Resources
Owner
- Name: Romain Strock
- Login: srom
- Kind: user
- Location: London, UK
- Website: https://romainstrock.com
- Twitter: romainstrock
- Repositories: 26
- Profile: https://github.com/srom
Scientist, AI enthusiast, Bioinformatics PhD student @ Imperial College
GitHub Events
Total
- Watch event: 15
- Fork event: 2
Last Year
- Watch event: 15
- Fork event: 2
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| srom | r****k@g****m | 63 |
| Romain Strock | s****m | 14 |
| Niklas Pirnay | n****s@p****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 1
- Total pull requests: 1
- Average time to close issues: 9 days
- Average time to close pull requests: 3 minutes
- Total issue authors: 1
- Total pull request authors: 1
- Average comments per issue: 3.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- n1kn4x (1)
Pull Request Authors
- srom (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 80 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 2
(may contain duplicates) - Total versions: 7
- Total maintainers: 1
pypi.org: cma-es
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implemented with TensorFlow
- Homepage: https://github.com/srom/cma-es
- Documentation: https://cma-es.readthedocs.io/
- License: MIT
-
Latest release: 1.5.0
published about 4 years ago
Rankings
Maintainers (1)
conda-forge.org: cma-es
- Homepage: https://github.com/srom/cma-es
- License: MIT
-
Latest release: 1.5.0
published about 4 years ago
Rankings
Dependencies
- tensorflow >=2.0
- jupyter *
- matplotlib *
- scipy *
- seaborn *
- tensorflow *