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arXiv 1912.03395q-bio.PEcs.NE
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基于自然选择的信息几何优化

Information-geometric optimization with natural selection

Jakub Otwinowski, Colin LaMont

AI总结:

本文将群体遗传学与进化优化结合,提出一种基于自然选择、群体重组和自适应增强选择的简单进化算法,无需矩阵求逆或协方差矩阵存储,性能与协方差矩阵自适应和自然进化策略相当。

摘要:

进化算法受自然进化启发,旨在不计算导数的情况下优化困难的目标函数。本文详细阐述了群体遗传学与进化优化之间的关系,并提出一种新的进化算法。连续目标函数的优化类似于在适应度景观上搜索高适应度表型。我们总结了自然选择如何沿着由群体在适应度景观上诱导的非欧几里得梯度(自然梯度)移动群体。在数量遗传学中常见的正态近似下,我们展示了选择如何与优化中的牛顿法相关联。我们发现中等强度的选择对适应度景观最具信息量。我们描述了新表型的生成,并引入一个重组算子,该算子重组整个群体以生成保持正态统计量的变体。最后,我们引入一个原理验证算法,该算法结合了自然选择、我们的重组算子以及一种自适应增强选择的方法。我们的算法在优化中类似于协方差矩阵自适应和自然进化策略,并具有相似的性能。该算法实现极其简单,无需矩阵求逆或分解,不需要存储协方差矩阵,并可能构成基于自然梯度更新的更一般基于模型的优化算法的基础。

英文摘要:

Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between population genetics and evolutionary optimization and formulate a new evolutionary algorithm. Optimization of a continuous objective function is analogous to searching for high fitness phenotypes on a fitness landscape. We summarize how natural selection moves a population along the non-euclidean gradient that is induced by the population on the fitness landscape (the natural gradient). Under normal approximations common in quantitative genetics, we show how selection is related to Newton's method in optimization. We find that intermediate selection is most informative of the fitness landscape. We describe the generation of new phenotypes and introduce an operator that recombines the whole population to generate variants that preserve normal statistics. Finally, we introduce a proof-of-principle algorithm that combines natural selection, our recombination operator, and an adaptive method to increase selection. Our algorithm is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, and has similar performance. The algorithm is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, and may form the basis of more general model-based optimization algorithms with natural gradient updates.

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