Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and \((1+\lambda )\). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the (\(1 + \lambda\)) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

图表示为遗传编程（GP）提供了几个理想的特性；多输出程序，代码重用的自然表示，以及在许多情况下，一种固有的中性漂移机制。每个图 GP 技术都提供了一个程序表示、遗传算子和总体进化算法。这使得很难确定经验差异的个体原因，无论是在这些方法之间还是与传统 GP 相比。在这项工作中，我们实证研究了笛卡尔遗传规划 (CGP)、线性遗传规划 (LGP)、通过图规划和传统 GP 进化图的行为。通过修复配置的某些方面，我们研究了每个图 GP 方法和 GP 结合三种不同 EA 的性能：分代、稳态和\((1+\lambda )\)。一般来说，我们发现表示、遗传算子和进化算法的最佳选择取决于问题域。此外，我们发现图 GP 方法可以提高复杂的现实世界回归问题的搜索性能，特别是与 ( \(1 + \lambda\) ) EA 结合使用时，在数字电路合成任务上明显更好。我们进一步表明，通过调整 LGP 的寄存器数量和 CGP 的级别回参数来重用中间结果是最重要的，并且在解决受益于代码重用的复杂问题时，对优化算法的更好收敛有显着贡献。