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A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming

机译：一种新的基于神经网络的符号回归方法：神经编码的表达式编程

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Neuro-encoded expression programming (NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expression programming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.
机译：本文提出了神经编码的表达编程（NEEP），旨在为遗传编程方法提供一种新颖的连续编码组合表示形式。具有线性表示的遗传编程使用自然启发的运算符（例如交叉，突变）来调整表达式，并最终搜索出最佳的显式函数以模拟数据。编码机制对于遗传编程有效地找到理想的解决方案至关重要。但是，线性表示方法在离散解空间中操纵表达式树，其中输入的较小更改会导致输出的较大更改。不平坦的景观破坏了当地信息，使搜索变得困难。神经编码的表达程序使用递归神经网络（RNN）构造基因串，并通过强大的连续进化算法优化网络的权重。神经网络映射可平滑锐利的健身环境，并提供丰富的邻域信息以找到最佳表达。实验表明，该新方法提高了训练效率，并减少了一些著名的符号回归问题的测试错误。

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