Effective Population and Training Data Partitioning in Parallel Distributed Evolutionary Knowledge Acquisition

进化知识获取是为了从数值数据中获取基于规则的知识而提出的。这种方法的主要问题是当我们将其应用于大数据时，需要巨大的计算成本。本研究提出了并行分布式实现进化知识获取的人口和训练数据被分为子群体和训练数据子集，分别。实验结果表明，该算法在不降低泛化能力的前提下，大大降低了计算量。并行分布式实现的各种规格的影响也检查。

Evolutionary knowledge acquisition has beenproposed in order to obtain rule-based knowledge from numerical data. The main problem of this method is that huge computation cost is necessarywhen we apply it to large data. This study proposes parallel distributed implementation of evolutionary knowledge acquisition where both a population and training data are divided into subpopulations and training data subsets, respectively. The computational experiments show that the computational cost can be drastically reduced without the deterioration of the generalization ability. The effects of various specifications for parallel distributed implementation are also examined.