概率语法演变
Probabilistic Grammatical Evolution
摘要：
语法进化（GE）是遗传编程（GP）中最流行的变体之一，它已经成功地应用于多个问题领域。自最初的提案以来，已经向GE提出了许多改进建议，以解决其一些主要问题并提高其绩效。在本文中，我们提出了概率语法进化（PGE），它引入了一个新的基因型表示和新的映射机制GE。具体来说，我们诉诸概率上下文无关文法（PCFG），它的概率是适应在进化过程中，考虑到生产选择构建最适合的个人。基因型是一个真实值的列表，其中每个值表示选择导出规则的可能性。我们在两个回归问题中评估了PGE的性能，并将其与GE和结构化语法进化（SGE）进行了比较。结果表明，PGE的性能优于GE，差异有统计学意义，与SGE相比，PGE的性能相近。
英文摘要：
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.
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