多实验计算模型的演化非支配参数集

Peter Lane, F. Gobet
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引用次数: 8

摘要

创建健壮的、可重复的、最优的计算模型是许多科学领域理论家面临的一个关键挑战。心理学和认知科学面临着特殊的挑战,因为收集了大量的数据,许多模型不适合用于计算参数集的分析技术。具体的问题是为给定的数据集找到所有可接受的模型参数,并确认不同数据集之间模型参数的一致性。解决这些问题将有助于更好地理解计算模型的行为,从而支持通用和健壮模型的开发。在本文中,我们使用进化算法来针对多组实验数据开发计算模型的参数来解决这些问题;特别地,我们在几个理论中提出了进化模型的“物种非支配排序遗传算法”。我们讨论了使用29组数据和从四个不同理论中得出的模型来开发分类模型的问题。我们发现,进化算法产生高质量的模型,适应提供一个很好的适合所有可用的数据。
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Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments
Abstract Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the ‘speciated non-dominated sorting genetic algorithm’ for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.
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