| Name | pyhms JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | https://github.com/maciejsmolka/pyhms |
| Summary | The HMS (Hierarchic Memetic Strategy) is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular evolutionary engine. This package provides a simple python implementation with examples of using different population engines. |
| upload_time | 2024-08-22 15:44:21 |
| maintainer | None |
| docs_url | None |
| author | Maciej Smołka |
| requires_python | <4.0,>=3.10 |
| license | MIT |
| keywords |
optimization
hms
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pyhms
![GitHub Test Badge][1] [![codecov][2]](https://codecov.io/gh/agh-a2s/pyhms) [![Documentation Status][3]](https://pyhms.readthedocs.io/en/latest/?badge=latest) [![pypi.org][4]][5] [![versions][6]][7] ![license][8]
[1]: https://github.com/agh-a2s/pyhms/actions/workflows/pytest.yml/badge.svg "GitHub CI Badge"
[2]: https://codecov.io/gh/agh-a2s/pyhms/graph/badge.svg?token=srsivvv2ff
[3]: https://readthedocs.org/projects/pyhms/badge/?version=latest
[4]: https://img.shields.io/pypi/v/pyhms.svg "Pypi Latest Version Badge"
[5]: https://pypi.python.org/pypi/pyhms "Pypi site"
[6]: https://img.shields.io/pypi/pyversions/pyhms.svg
[7]: https://github.com/agh-a2s/pyhms
[8]: https://img.shields.io/github/license/agh-a2s/pyhms
`pyhms` is a Python implementation of Hierarchic Memetic Strategy (HMS).
The Hierarchic Memetic Strategy is a stochastic global optimizer designed to tackle highly multimodal problems. It is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular optimization engine. This package provides a simple python implementation.
### Installation
Installation can be done using `pypi`:
```
pip install pyhms
```
It's also possible to install the current main branch:
```
pip install git+https://github.com/agh-a2s/pyhms.git@main
```
### Quick Start
```python
from pyhms import minimize
import numpy as np
fun = lambda x: sum(x**2)
bounds = np.array([(-20, 20), (-20, 20)])
solution = minimize(
fun=fun,
bounds=bounds,
maxfun=10000,
log_level="debug",
seed=42
)
```
`pyhms` provides an interface similar to `scipy.optimize.minimize`. This is the simplest way to run HMS with default parameters.
```python
import numpy as np
from pyhms import (
EALevelConfig,
hms,
get_NBC_sprout,
DontStop,
MetaepochLimit,
SEA,
Problem,
)
square_bounds = np.array([(-20, 20), (-20, 20)])
square_problem = Problem(lambda x: sum(x**2), maximize=False, bounds=square_bounds)
config = [
EALevelConfig(
ea_class=SEA,
generations=2,
problem=square_problem,
pop_size=20,
mutation_std=1.0,
lsc=DontStop(),
),
EALevelConfig(
ea_class=SEA,
generations=4,
problem=square_problem,
pop_size=10,
mutation_std=0.25,
sample_std_dev=1.0,
lsc=DontStop(),
),
]
global_stop_condition = MetaepochLimit(limit=10)
sprout_condition = get_NBC_sprout(level_limit=4)
hms_tree = hms(config, global_stop_condition, sprout_condition)
print(hms_tree.summary())
```
### Relevant literature
- J. Sawicki, M. Łoś, M. Smołka, R. Schaefer. Understanding measure-driven algorithms solving irreversibly ill-conditioned problems. Natural Computing 21:289-315, 2022. doi: [10.1007/s11047-020-09836-w](https://doi.org/10.1007/s11047-020-09836-w)
- J. Sawicki, M. Łoś, M. Smołka, J. Alvarez-Aramberri. Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations. Journal of computational science, 34, 48-54, 2019. doi: [10.1016/j.jocs.2019.04.005](https://doi.org/10.1016/j.jocs.2019.04.005)
Raw data
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"description": "# pyhms\n![GitHub Test Badge][1] [![codecov][2]](https://codecov.io/gh/agh-a2s/pyhms) [![Documentation Status][3]](https://pyhms.readthedocs.io/en/latest/?badge=latest) [![pypi.org][4]][5] [![versions][6]][7] ![license][8]\n\n[1]: https://github.com/agh-a2s/pyhms/actions/workflows/pytest.yml/badge.svg \"GitHub CI Badge\"\n[2]: https://codecov.io/gh/agh-a2s/pyhms/graph/badge.svg?token=srsivvv2ff\n[3]: https://readthedocs.org/projects/pyhms/badge/?version=latest\n[4]: https://img.shields.io/pypi/v/pyhms.svg \"Pypi Latest Version Badge\"\n[5]: https://pypi.python.org/pypi/pyhms \"Pypi site\"\n[6]: https://img.shields.io/pypi/pyversions/pyhms.svg\n[7]: https://github.com/agh-a2s/pyhms\n[8]: https://img.shields.io/github/license/agh-a2s/pyhms\n\n`pyhms` is a Python implementation of Hierarchic Memetic Strategy (HMS).\n\nThe Hierarchic Memetic Strategy is a stochastic global optimizer designed to tackle highly multimodal problems. It is a composite global optimization strategy consisting of a multi-population evolutionary strategy and some auxiliary methods. The HMS makes use of a dynamically-evolving data structure that provides an organization among the component populations. It is a tree with a fixed maximal height and variable internal node degree. Each component population is governed by a particular optimization engine. This package provides a simple python implementation.\n\n### Installation\nInstallation can be done using `pypi`:\n```\npip install pyhms\n```\nIt's also possible to install the current main branch:\n```\npip install git+https://github.com/agh-a2s/pyhms.git@main\n```\n\n### Quick Start\n\n```python\nfrom pyhms import minimize\nimport numpy as np\n\nfun = lambda x: sum(x**2)\nbounds = np.array([(-20, 20), (-20, 20)])\nsolution = minimize(\n fun=fun,\n bounds=bounds,\n maxfun=10000,\n log_level=\"debug\",\n seed=42\n)\n```\n\n`pyhms` provides an interface similar to `scipy.optimize.minimize`. This is the simplest way to run HMS with default parameters.\n\n```python\nimport numpy as np\nfrom pyhms import (\n EALevelConfig,\n hms,\n get_NBC_sprout,\n DontStop,\n MetaepochLimit,\n SEA,\n Problem,\n)\n\nsquare_bounds = np.array([(-20, 20), (-20, 20)])\nsquare_problem = Problem(lambda x: sum(x**2), maximize=False, bounds=square_bounds)\n\nconfig = [\n EALevelConfig(\n ea_class=SEA,\n generations=2,\n problem=square_problem,\n pop_size=20,\n mutation_std=1.0,\n lsc=DontStop(),\n ),\n EALevelConfig(\n ea_class=SEA,\n generations=4,\n problem=square_problem,\n pop_size=10,\n mutation_std=0.25,\n sample_std_dev=1.0,\n lsc=DontStop(),\n ),\n]\nglobal_stop_condition = MetaepochLimit(limit=10)\nsprout_condition = get_NBC_sprout(level_limit=4)\nhms_tree = hms(config, global_stop_condition, sprout_condition)\nprint(hms_tree.summary())\n```\n\n### Relevant literature\n\n- J. Sawicki, M. \u0141o\u015b, M. Smo\u0142ka, R. Schaefer. Understanding measure-driven algorithms solving irreversibly ill-conditioned problems. Natural Computing 21:289-315, 2022. doi: [10.1007/s11047-020-09836-w](https://doi.org/10.1007/s11047-020-09836-w)\n- J. Sawicki, M. \u0141o\u015b, M. Smo\u0142ka, J. Alvarez-Aramberri. Using Covariance Matrix Adaptation Evolutionary Strategy to boost the search accuracy in hierarchic memetic computations. Journal of computational science, 34, 48-54, 2019. doi: [10.1016/j.jocs.2019.04.005](https://doi.org/10.1016/j.jocs.2019.04.005)\n",
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