https://github.com/zevv/nimcmaes
Nim CMA-ES library
https://github.com/zevv/nimcmaes
Last synced: 5 months ago
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Nim CMA-ES library
- Host: GitHub
- URL: https://github.com/zevv/nimcmaes
- Owner: zevv
- Created: 2020-06-25T18:57:56.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-26T19:03:50.000Z (over 5 years ago)
- Last Synced: 2025-10-04T11:46:20.023Z (5 months ago)
- Language: C
- Size: 760 KB
- Stars: 6
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
This is a minimal Nim binding for the C-CMA-ES library
> "_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._"
- http://cma.gforge.inria.fr/cmaesintro.html
- http://cma.gforge.inria.fr/cmaes_sourcecode_page.html
- https://github.com/cma-es/c-cmaes
## Introduction
The CMA-ES algorithm can be used to find solutions to any N-dimensional
optimization problem.
In practical terms:
- The user provides a fitness function which calculates the error output for a
given problem with N continuous inputs
- The CMA-ES algorithm makes up values for the inputs, calls the fit function
and inspects the returned error
- The input variables are adjusted depending on the error value
- Step 2 and 3 are repeated until the algorithm finds the best solution
matching your error function
For now this library offers only the bare minimum functionality of the
underlying C library. Also, I do not actually understand any of the
implementation, I just use this library to solve real-world problems.
## Example
```nim
import nimcmaes
import math
# This is the fit function: It is passed an number of floats which have some
# meaning for the problem you are trying to solve. This function should
# calculate the error for a target from the inputs for your problem. The closer
# to 0, the better the input values.
proc fitFun(v: openArray[float]): float =
# Rosenbrock function
let (x, y) = (v[0], v[1])
result = pow(1.0 - x, 2) + 100.0 * pow(y - x * x, 2)
# Run the CMAES algorithm
let
start = [0.0, 0.0] # These are the start values
stddev = [1.0, 1.0] # And the expected standard deviation
let xbest = cmaesRun(start, stddev, fitFun)
echo xbest
```