Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create sub-optimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This paper presents Grammar-based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.

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基于语法的遗传编程中的概率上下文和结构依赖学习

遗传编程是一种基于进化原理自动创建计算机程序的方法。由程序组件之间的复杂依赖引起的欺骗性问题具有挑战性。这很重要，因为它可能会误导遗传编程以创建次优程序。此外，程序中的微小修改可能会导致程序行为的显着变化并影响最终输出。本文介绍了基于语法的贝叶斯分类器遗传编程 (GBGPBC)，其中使用一组贝叶斯网络分类器捕获程序组件之间的概率依赖性。我们的系统使用一组基准问题（欺骗性最大值问题、皇家树问题和双极不对称皇家树问题）进行评估。就适应度评估的总数而言，它在搜索最佳程序方面通常比其他相关的遗传编程方法更健壮和更有效。我们研究了哪些因素会影响 GBGPBC 的性能，并发现 GBGPBC 的强大变体与某些复杂性度量始终弱相关。此外，我们的方法已应用于学习直接营销中一组客户的排名程序。与由神经网络、逻辑回归和贝叶斯网络等多种知名机器学习算法生成的其他解决方案相比，我们建议的解决方案可帮助公司获得更多收益。我们研究了哪些因素会影响 GBGPBC 的性能，并发现 GBGPBC 的强大变体与某些复杂性度量始终弱相关。此外，我们的方法已应用于学习直接营销中一组客户的排名程序。与由神经网络、逻辑回归和贝叶斯网络等多种知名机器学习算法生成的其他解决方案相比，我们建议的解决方案可帮助公司获得更多收益。我们研究了哪些因素会影响 GBGPBC 的性能，并发现 GBGPBC 的强大变体与某些复杂性度量始终弱相关。此外，我们的方法已应用于学习直接营销中一组客户的排名程序。与由神经网络、逻辑回归和贝叶斯网络等多种知名机器学习算法生成的其他解决方案相比，我们建议的解决方案可帮助公司获得更多收益。