论文标题
多目标遗传编程中语义的亮点
Highlights of Semantics in Multi-objective Genetic Programming
论文作者
论文摘要
语义是遗传编程（GP）研究的越来越多的领域，是指执行遗传编程人员的行为输出。这项研究通过提出一种新方法来扩展对语义的当前理解：基于语义的距离作为附加标准（SDO），迄今为止，在多目标GP（MOGP）中的语义研究领域有限有限。我们的工作包括在性能和多样性指标方面对GP进行广泛的分析，该方法使用了另外基于语义的方法，即基于语义相似性的跨界（SCC）和基于语义的拥挤距离（SCD）。每种方法都集成到两个进化的多目标（EMO）框架中：非主导的分类遗传算法II（NSGA-II）和强度帕累托进化算法2（SPEA2），以及三种语义方法，以及三种nsga-ii和Spea2的典型形式。我们使用高度不平衡的二元分类数据集，我们证明了SDO的新提出的方法始终生成更非主导的解决方案，具有更好的多样性和改进的超量结果。
Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in Multi-objective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.
