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Implementation of Lamarckian concepts in a Genetic Algorithm for structure solution from powder diffraction data

机译:基于粉末衍射数据的结构求解遗传算法中Lamarckian概念的实现

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Previous implementations of Genetic Algorithms in direct-space strategies for structure solution from powder diffraction data have employed the operations of mating, mutation and natural selection, with the fitness of each structure based on comparison between calculated and experimental powder diffraction patterns (we define fitness as a function of weighted-profile R-factor R-wp). We report an extension to this method, in which each structure generated in the Genetic Algorithm is subjected to local minimization of R-wp with respect to structural variables. This approach represents an implementation of Lamarckian concepts of evolution, and is found to give significant improvements in efficiency and reliability. (C) 2000 Elsevier Science B.V. All rights reserved. [References: 30]
机译:以前在直接空间策略中基于粉末衍射数据进行结构求解的遗传算法实现方法已经采用了交配,变异和自然选择的操作,每种结构的适应性基于计算的粉末衍射图和实验粉末衍射图之间的比较(我们将适应度定义为加权轮廓R因子R-wp的函数)。我们报告此方法的扩展,其中遗传算法中生成的每个结构都相对于结构变量受到R-wp的局部最小化。这种方法代表了Lamarckian进化概念的实现,并且被发现在效率和可靠性方面有显着提高。 (C)2000 Elsevier Science B.V.保留所有权利。 [参考:30]

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