The requirements of Machine Learning applications are changing rapidly. Machine Learning models need to deal with increasing volumes of data, and need to do so quicker as responses are expected more than ever in real-time. Plus, sources of data are becoming more and more dynamic, with patterns thatchange more frequently. This calls for new approaches and algorithms, that are able to efficiently deal with these challenges. In this paper we propose the use of a Genetic Algorithm to Optimize a Stacking Ensemble specifically developed for streaming scenarios. A pool of solutions is maintained in which each solution represents a distribution of weights in the ensemble. The Genetic Algorithm continuously optimizes these weights to minimize the cost function. Moreover, new models are added at regular intervals, trained on more recent data. These models eventually replace older and less accurate ones, making the ensemble adapt continuously do changes in the distribution of the data.

中文翻译：

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在数据流方案中使用遗传算法优化堆栈集成

机器学习应用程序的需求正在迅速变化。机器学习模型需要处理不断增长的数据量，并且需要更快地处理，因为实时响应的期望比以往任何时候都高。另外，数据源变得越来越动态，模式变化也越来越频繁。这需要能够有效应对这些挑战的新方法和算法。在本文中，我们建议使用遗传算法来优化专门为流方案开发的堆栈集成。维持一个解决方案池，其中每个解决方案代表整体中的权重分布。遗传算法不断优化这些权重以最小化成本函数。此外，会定期更新新模型，并根据最新数据进行训练。