# Atoms toolbox CMA-ES released for Scilab 6 and 2023

**URL:** <https://scilab.discourse.group/t/atoms-toolbox-cma-es-released-for-scilab-6-and-2023/80>\
**Category:** Atoms packages\
**Created:** [April 13, 2023, 6:28pm UTC](https://scilab.discourse.group/t/atoms-toolbox-cma-es-released-for-scilab-6-and-2023/80 "2023-04-13T18:28:10Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![cfuttrup](https://avatars.discourse-cdn.com/v4/letter/c/3ec8ea/32.png) [@cfuttrup](https://scilab.discourse.group/u/cfuttrup)\
**Post date:** [April 13, 2023, 6:28pm UTC](https://scilab.discourse.group/t/atoms-toolbox-cma-es-released-for-scilab-6-and-2023/80/1 "2023-04-13T18:28:10Z")

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I packed CMA-ES in 2014 for Scilab 5.3, but it didn’t work for Scilab 6. Thank you to Stephane Mottelet and Clement David for helping to update CMA-ES so it now runs under Scilab 6, and for helping to package a module properly.

CMA-ES is the most powerful black-box optimization routine available, as proved by many years of dominance in the BBOB competitions. Originally so-called genetic algorithms provided smart black-box optimization, and later optimization routines typically had a decade of being ‘best’ like particle swarm optimization, and simulated annealing, etc. CMA-ES seems to the so powerful, nobody was able to formulate a better general-purpose black-box optimization routine.

Have fun with it.

Cheers,  
Claus

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**Author:** ![davcheze](https://avatars.discourse-cdn.com/v4/letter/d/90db22/32.png) [@davcheze](https://scilab.discourse.group/u/davcheze)\
**Post date:** [April 18, 2023, 4:12pm UTC](https://scilab.discourse.group/t/atoms-toolbox-cma-es-released-for-scilab-6-and-2023/80/2 "2023-04-18T16:12:48Z")

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Hello Claus,

I tried to access the link (in ATOMS module’s description) about intro to CMA -ES but it seems is broken…  
I found in HAL for LRI about CMA ES from Nikolaus Hansen. The CMA Evolution Strategy: A Tutorial. 2005. ⟨hal-01297037v2⟩

> **[The CMA Evolution Strategy: A Tutorial](https://hal-centralesupelec.archives-ouvertes.fr/UMR8623/hal-01297037v2)**
>
> This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We...

and Oswin Krause, Tobias Glasmachers, Nikolaus Hansen, Christian Igel. Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite. GECCO’16 - Companion of Proceedings of the 2016 Genetic and Evolutionary Computation Conference, ACM, Jul 2016, Denver, United States. pp.1177-1184, ⟨10.1145/2908961.2931699⟩. ⟨hal-01381653⟩

> **[Unbounded Population MO-CMA-ES for the Bi-Objective BBOB Test Suite](https://hal-centralesupelec.archives-ouvertes.fr/UMR8623/hal-01381653v1)**
>
> The unbounded population multi-objective covariance matrix adaptation evolution strategy (UP-MO-CMA-ES) aims at maximizing the total hypervolume covered by all evaluated points. It adds all non-dominated solutions found to its population and employs...

Thanks for your indication  
David

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<div class="post-metadata">

**Author:** ![cfuttrup](https://avatars.discourse-cdn.com/v4/letter/c/3ec8ea/32.png) [@cfuttrup](https://scilab.discourse.group/u/cfuttrup)\
**Post date:** [April 18, 2023, 5:15pm UTC](https://scilab.discourse.group/t/atoms-toolbox-cma-es-released-for-scilab-6-and-2023/80/3 "2023-04-18T17:15:04Z")

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Hi David  
The link has changed since 2014, it is now:  
[http://www.cmap.polytechnique.fr/~nikolaus.hansen/cmaesintro.html](http://www.cmap.polytechnique.fr/~nikolaus.hansen/cmaesintro.html)

I logged into atoms and was able to update the description.

Cheers,  
Claus