多变量到任意部分指定随机向量生成的进化策略

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) Pub Date : 2004-06-19 DOI:10.1109/CEC.2004.1331175

S. Stanhope

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引用次数: 2

摘要

部分指定随机向量生成的多变量任意方法通过将样本从驱动分布转换为具有给定边际和相关性特征的样本来工作。变换后的随机向量的相关性由驱动分布控制;对部分指定的随机向量进行采样需要找到合适的驱动分布。本文鼓励使用进化策略来解决这类问题，并将进化策略与求解Dirichlet-to-anything变换的共轭梯度方法进行比较。结果表明，演化策略在求解参数化问题时至少与共轭梯度法一样有效。

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Evolution strategies for multivariate-to-anything partially specified random vector generation

Multivariate-to-anything methods for partially specified random vector generation work by transforming samples from a driving distribution into samples characterized by given marginals and correlations. The correlations of the transformed random vector are controlled by the driving distribution; sampling a partially specified random vector requires finding an appropriate driving distribution. This paper motivates the use of evolution strategies for solving such problems and compares evolution strategies to conjugate gradient methods in the context of solving a Dirichlet-to-anything transformation. It is shown that the evolution strategy is at least as effective as the conjugate gradient method for solution of the parameterization problem.

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Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

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