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Learning environment dynamics from self-adaptation. A preliminary investigation

Amine Boumaza 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : We present an experimental study that shows a relationship between the dynamics of the environment and the adaptation of strategy parameters. Experiments conducted on two adaptive evolutionary strategies SA-ES and CMA-ES on the dynamic sphere function, show that the nature of the movements of the function's optimum are reflected in the evolution of the mutation steps. Three types of movements are presented: constant, linear and quadratic velocity, in all, the evolution of mutation steps during adaptation reflect distinctly the nature of the movements. Furthermore with CMA-ES, the direction of movement of the optimum can be extracted.
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Contributor : Amine Boumaza <>
Submitted on : Tuesday, November 8, 2005 - 7:31:45 PM
Last modification on : Friday, February 26, 2021 - 3:28:04 PM
Long-term archiving on: : Friday, April 2, 2010 - 6:45:39 PM


  • HAL Id : inria-00000618, version 1



Amine Boumaza. Learning environment dynamics from self-adaptation. A preliminary investigation. GECCO'05 Workshop on Evolutionary Algorithms for Dynamic Optimization Problems - EvoDOP, Jun 2005, Washington DC/USA. ⟨inria-00000618⟩



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