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A formal approach to deriving factored evolutionary algorithm architectures

机译：推导因式进化算法体系结构的正式方法

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Factored Evolutionary Algorithms (FEA) are a class of evolutionary search-based optimization algorithms that have been applied successfully to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. In this paper, we provide a formal method for deriving factor architectures and give theoretical justification for its use. Specifically, we utilize factor graphs of variables in probabilistic graphical models as a way to define factor architectures. We also prove how a class of problems, like maximizing NK landscapes, are equivalent to abductive inference in probabilistic graphical models. This allows us to take a factor graph architecture and apply it to NK landscapes and a set of commonly used benchmark functions. Finally, we show empirically that using the factor graph representation to derive factors for FEA provides the best performance in the majority of cases studied.
机译：因子进化算法（FEA）是一类基于进化搜索的优化算法，已成功应用于各种问题，例如训练神经网络和在图形模型中执行归纳推理。 FEA的独特之处在于，它通过创建重叠的子群体来优化目标函数，这些子群体在函数变量的子集上进行了优化。使用FEA的一个考虑因素是确定适当的因子体系结构，该体系结构确定每个因子将优化的变量集。在本文中，我们提供了一种推导因子体系结构的形式化方法，并为其使用提供了理论依据。具体来说，我们利用概率图形模型中变量的因子图作为定义因子体系结构的一种方法。我们还证明了诸如最大化NK景观等一类问题如何等效于概率图形模型中的归纳推理。这使我们能够采用因子图架构，并将其应用于NK景观和一组常用的基准函数。最后，我们凭经验表明，在大多数研究的案例中，使用因子图表示法得出FEA的因子可提供最佳性能。

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