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CMA-ES

An algorithm for difficult non-linear non-convex optimization problems.
(2428 downloads for this version - 5198 downloads for all versions)
Details
Version
1.4
Author
Claus Futtrup
Owner Organization
SEAS Fabrikker A/S
Maintainer
Claus Futtrup
License
Creation Date
December 8, 2014
Source created on
Scilab 5.5.x
Binaries available on
Scilab 5.5.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit MacOSX
Install command
--> atomsInstall("CMA-ES")
Description
             Purpose
 -------

 This document introduces the CMA-ES Optimization (CMA-ES) in Scilab.

 The CMA-ES is a meta-heuristic optimization process created by Nikolaus Hansen
 initially (1,lambda)-ES in 1996 (micro = 1), later with (micro,lambda) in 1997
 and it is based on a Covariance Matrix Adaptation technique.

 This direct search method does not require any knowledge of the  objective
 function derivatives.

 The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an
evolutionary
 algorithm for difficult non-linear non-convex optimization problems in
 continuous domain. The CMA-ES is typically applied to unconstrained or bounded
 constraint optimization problems, and search space dimensions between three
and
 a hundred. The method should be applied, if derivative based methods, e.g.
 quasi-Newton BFGS or conjugate gradient, (supposedly) fail due to a rugged
 search landscape (e.g. discontinuities, sharp bends or ridges, noise, local
 optima, outliers). If second order derivative based methods are successful,
 they are usually faster than the CMA-ES

 This toolbox implements the original CMA-ES algorithm in two ways:

  * the functional call (similar to Scilab fminsearch, but not exactly the
same)
  * the object oriented call sequence (as described by Yann Collette)

 The functional call supports additionally re-execution and population
increase.

 See https://www.lri.fr/~hansen/cmaesintro.html for
details.            
Files (2)
[105.19 kB]
Source code archive

[88.69 kB]
OS-independent binary for Scilab 5.5.x
Binary version
Automatically generated by the ATOMS compilation chain

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