Code bloat is a phenomenon in Genetic Programming (GP) characterized by the increase in individual size during the evolutionary process without a corresponding improvement in fitness. Bloat negatively affects GP performance, since large individuals are more time consuming to evaluate and harder to interpret. In this paper, we propose two approaches for reducing GP code bloat based on a semantic approximation technique. The first approach replaces a random subtree in an individual by a smaller tree of approximate semantics. The second approach replaces a random subtree by a smaller tree that is semantically approximate to the desired semantics. We evaluated the proposed methods on a large number of regression problems. The experimental results showed that our methods help to significantly reduce code bloat and improve the performance of GP compared to standard GP and some recent bloat control methods in GP. Furthermore, the performance of the proposed approaches is competitive with the best machine learning technique among the four tested machine learning algorithms.

代码膨胀是遗传编程（GP）中的一种现象，其特征是在进化过程中个体大小增加，而适应性却没有相应提高。膨胀对GP表现有负面影响，因为大型个体评估起来比较耗时，而且难以解释。在本文中，我们提出了两种基于语义近似技术的减少GP代码膨胀的方法。第一种方法是用较小的近似语义树替换个体中的随机子树。第二种方法是用语义上近似于所需语义的较小树替换随机子树。我们对大量回归问题评估了所提出的方法。实验结果表明，与标准GP和GP中的一些最近的膨胀控制方法相比，我们的方法有助于显着减少代码膨胀并提高GP的性能。此外，所提出的方法的性能与四种经过测试的机器学习算法中的最佳机器学习技术相比具有竞争力。