In evolutionary computation, it is commonly assumed that a search algorithm acquires knowledge about a problem instance by sampling solutions from the search space and evaluating them with a fitness function. This is necessarily inefficient because fitness reveals very little about solutions -- yet they contain more information that can be potentially exploited. To address this observation in genetic programming, we propose EvoNUDGE, which uses a graph neural network to elicit additional knowledge from symbolic regression problems. The network is queried on the problem before an evolutionary run to produce a library of subprograms, which is subsequently used to seed the initial population and bias the actions of search operators. In an extensive experiment on a large number of problem instances, EvoNUDGE is shown to significantly outperform multiple baselines, including the conventional tree-based genetic programming and the purely neural variant of the method.

翻译：在进化计算中，通常假设搜索算法通过从搜索空间中采样解并用适应度函数评估它们来获取关于问题实例的知识。这必然是低效的，因为适应度揭示的关于解的信息非常有限——然而解本身包含更多可被潜在利用的信息。为了在遗传规划中应对这一观察，我们提出了EvoNUDGE，它使用图神经网络从符号回归问题中提取额外知识。该网络在进化运行前对问题进行查询，以生成一个子程序库，随后用于初始化种群并偏置搜索算子的操作。在大量问题实例上的广泛实验中，EvoNUDGE被证明显著优于多个基线方法，包括传统的基于树的遗传规划以及该方法的纯神经变体。