The worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose a novel Multi-objective Optimization (MO) approach to evolve Decision Trees (DT) using a Grammatical Evolution (GE), under two main variants: a pure GE method (MGEDT) and a GE with Lamarckian Evolution (MGEDTL). Both variants evolve variable-length DTs and perform a simultaneous optimization of the predictive performance and model complexity. To handle big data, the GE methods include a training sampling and parallelism evaluation mechanism. The algorithms were applied to a recent database with around 6 million records from a real-world DSP. Using a realistic Rolling Window (RW) validation, the two GE variants were compared with a standard DT algorithm (CART), a Random Forest and a state-of-the-art Deep Learning (DL) model. Competitive results were obtained by the GE methods, which present affordable training times and very fast predictive response times.

移动设备在全球范围内的采用正在提高移动性能营销的价值，这得益于将移动用户与广告匹配的需求方平台（DSP）的支持。在这些市场中，仅当有用户转换时才发生货币补偿。因此，一个关键的DSP问题是设计一个数据驱动模型来预测用户转换。为了处理这项艰巨的任务，我们提出了一种新颖的多目标优化（MO）方法，该方法使用语法演化（GE）来演化决策树（DT），主要有以下两种变体：纯GE方法（MGEDT）和带有Lamarckian的GE进化（MGEDTL）。两种变体都演化出可变长度DT，并同时优化预测性能和模型复杂性。为了处理大数据，GE方法包括训练采样和并行度评估机制。将该算法应用于最近的数据库中，该数据库具有来自现实世界DSP的大约600万条记录。使用逼真的滚动窗口（RW）验证，将这两个GE变体与标准DT算法（CART），随机森林和最新的深度学习（DL）模型进行了比较。通过GE方法获得了具有竞争力的结果，该方法具有可承受的培训时间和非常快的预测响应时间。