Learning the complete-basis-functions parameterization for the optimization of dynamic molecular alignment by ES

Authors:
Ofer M. Shir;Joost N. Kok;Thomas Bäck;Marc J. J. Vrakking
Affiliations:
Natural Computing Group, Leiden University, Leiden, The Netherlands;Natural Computing Group, Leiden University, Leiden, The Netherlands;Natural Computing Group, Leiden University, Leiden, The Netherlands;Institute for Atomic and Molecular Physics, Amolf-FOM, Amsterdam, The Netherlands
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

This study further investigates the complete-basis-functions parameterization method (CBFP) for Evolution Strategies (ES), and its application to a challenging real-life high-dimensional physics optimization problem, namely Femtosecond Laser Pulse Shaping. The CBFP method, which was introduced recently for tackling efficiently the learning task of n-variables functions, is combined here, for the first time, with niching techniques, and shown to boost the learning process of the given laser problem, and to yield satisfying multiple optima. Moreover, a technique for learning the basis-functions and improving this method is outlined.