Bump cmaes from 0.8.2 to 0.11.1

[dependabot]

Bumps cmaes from 0.8.2 to 0.11.1.

Release notes

Sourced from cmaes's releases.

v0.11.1

What's Changed

• fix the weight of CatCMA by @​ha-mano in CyberAgentAILab/cmaes#184
• Bump the version up to v0.11.1 by @​c-bata in CyberAgentAILab/cmaes#185

Full Changelog: CyberAgentAILab/cmaes@v0.11.0...v0.11.1

PyPI: https://pypi.org/project/cmaes/0.11.1/

v0.11.0

Highlights

CatCMA [Hamano+, GECCO2024]

arXiv: https://arxiv.org/pdf/2405.09962

CatCMA is a method for mixed-category optimization problems, which is the problem of simultaneously optimizing continuous and categorical variables. CatCMA employs the joint probability distribution of multivariate Gaussian and categorical distributions as the search distribution.

Usage is like below:

import numpy as np
from cmaes import CatCMA
def sphere_com(x, c):
dim_co = len(x)
dim_ca = len(c)
if dim_co < 2:
raise ValueError("dimension must be greater one")
sphere = sum(x * x)
com = dim_ca - sum(c[:, 0])
return sphere + com
def rosenbrock_clo(x, c):
dim_co = len(x)
dim_ca = len(c)
if dim_co < 2:
raise ValueError("dimension must be greater one")
rosenbrock = sum(100 * (x[:-1] ** 2 - x[1:]) ** 2 + (x[:-1] - 1) ** 2)
clo = dim_ca - (c[:, 0].argmin() + c[:, 0].prod() * dim_ca)
return rosenbrock + clo
def mc_proximity(x, c, cat_num):
dim_co = len(x)
</tr></table>

... (truncated)

Commits

• a6e791a Merge pull request #185 from CyberAgentAILab/bump-to-v0.11.1
• 60c3d25 Bump the version up to v0.11.1
• ce8f5ff Merge pull request #184 from ha-mano/catcma_fix2
• fdc7461 Update _catcma.py
• 7513df8 Merge pull request #183 from CyberAgentAILab/bump-to-v0.11.0
• 711ca1b Bump the version up to v0.11.0
• 8fcb52d Merge pull request #181 from ha-mano/readme-catcma
• 4beb745 add the reference of CatCMA
• 387a138 Update README.md
• 8393eb3 Delete .DS_Store
• Additional commits viewable in compare view

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