Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance. In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE). The results show that PGE has a a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE.

翻译：格言进化(GE)是最为流行的基因方案变异(GP)之一,在几个问题领域得到了成功使用。自最初的提案以来,已经向GE提出了许多改进建议,以解决其中的一些主要问题并改进其绩效。在本文件中,我们提出了概率性格进化(PGE),为GE引入了新的基因组代言和新的绘图机制。具体地说,我们采用一种无概率性环境格(PCFG),在进化过程中,考虑到所选择的建造适中个体的产物,对它的概率进行了调整。基因型是一个真实值清单,其中每个值都代表选择衍生规则的可能性。我们评估了PGE在两个回归问题中的性能,并将其与GE和结构性格进化演化(SGE)进行比较。结果显示,与SGE相比,PGE的性能优于GE,具有统计上的重大差异,在与SGE相比时也取得了类似的表现。