Python的CMA-ES扩展1.0版本文档

https://www.lri.fr/~hansen/html-pythoncma/

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Nikolaus Hansen

Researcher at InriaInria research centre Saclay – Île-de-France 
Machine Learning and Optimization group (TAO) 
Universitè Paris-Sud, LRI (UMR8623) 
<a href="https://maps.google.fr/maps/ms?msid=208101493055643084316.0004d7c27dfc2f9ebb9e1&msa=0&ll=48.705,2.175&spn=0.0096,0.015" "="">Rue Noetzlin, Bât. (building) 660 (Digiteo-Moulon, entrance), room 2050. 
91405 Orsay Cedex, France 
mailto 

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Research objective. I want to understand stochastic search in continuous, high-dimensional search spaces. When improving known or developing new algorithms, I strongly care about whether and how they are useful in practice. Typical research means are statistical machine learning techniques, Markov chain analysis, meaningful comparison methodologies, and adaptive parameter control, eventually bridging the gap to the practitioners.

Inria is a French, public, science and technology research institution fully dedicated to research in computational science.

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• Publications
  • List of publications (some with source code)
  • via Google Scholar
  • via HAL open archive (from 2008)
  • via DBLP Digital Bibliography and Library Project
  • Talks, seminars, tutorials... (most including slides)
• CMA-ES (Covariance Matrix Adaptation Evolution Strategy)
  • A short introduction with further links and a commented publication list
  • A longer introduction on Wikipedia
  • Source code page
  • The mathematical foundation of CMA-ES can be found from information geometry, see Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles.
  • Related stuff
    • On the CMSA (Covariance Matrix Self-Adaptation) Evolution Strategy (2012)
    • On self-adaptation and derandomized self-adaptation (2002)
• Benchmarking continuous optimization algorithms
  • The COCO platform (COmparing Continuous Optimizers) for benchmarking real-parameter black-box optimization algorithms
  • Slides (4MB): A few overview results from the GECCO BBOB workshops.
  • Two galleries of all results from 2013.
  • BBOB-2010: Black-Box Optimization Benchmarking of real-parameter optimization algorithms for GECCO 2010
  • BBOB-2009: Black-Box Optimization Benchmarking of real-parameter optimization algorithms for GECCO 2009
  • Paper (pdf): Comparison of 31 Algorithms on the COCO testbed (BBOB-2009)
  • CEC-2005: Comparison of Evolutionary Algorithms for the 2005 IEEE Congress on Evolutionary Computation

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last change $Date: 2014-06-06 02:32:55 +0200 (Fri, 06 Jun 2014) $