首页> 外文会议>Intelligent Data Engineering and Automated Learing(IDEAL 2006); Lecture Notes in Computer Science; 4224 >Learning the Complete-Basis-Functions Parameterization for the Optimization of Dynamic Molecular Alignment by ES
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Learning the Complete-Basis-Functions Parameterization for the Optimization of Dynamic Molecular Alignment by ES

机译:学习完整基函数参数化以通过ES优化动态分子比对

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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.rnThe 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.
机译:这项研究进一步研究了用于进化策略(ES)的完整基函数参数化方法(CBFP),并将其应用于具有挑战性的现实高维物理优化问题,即飞秒激光脉冲整形.rnCBFP方法最近为有效解决n变量函数的学习任务而引入的方法,在这里首次与小生技术相结合,并显示出可以增强给定激光问题的学习过程,并产生令人满意的多重最优性。此外,概述了用于学习基本功能并改进该方法的技术。

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