论文标题
促进基于分解的多目标遗传编程中的语义
Promoting Semantics in Multi-objective Genetic Programming based on Decomposition
论文作者
论文摘要
遗传程序中语义的研究（GP）涉及一组投入的程序的行为，并在帮助促进GP的多样性方面得到了广泛报道，以解决一系列复杂问题，最终改善了进化搜索。这些研究中的绝大多数都将注意力集中在单目标GP上，只有少数例外，基于帕累托的主导算法（例如NSGA-II和SPEA2）已被用作框架来测试诸如基于语义相似性的基于语义相似性的跨越（SSC）（ssc）等众所周知的基于语义的方法，有助于或帮助进化。令人惊讶的是，据报道，SSC在SOGP中所表现出的好处在基于帕累托的多目标GP中没有看到。在这项工作中，我们有兴趣研究基于分解的多目标进化算法（MOEA/D）。通过使用机器学习社区中使用的众所周知的数据集MNIST数据集，我们展示了MOEA/D中的SSC如何促进语义多样性，与在规范MOEA/D中不存在的情况相比，它会产生更好的结果。
The study of semantics in Genetic Program (GP) deals with the behaviour of a program given a set of inputs and has been widely reported in helping to promote diversity in GP for a range of complex problems ultimately improving evolutionary search. The vast majority of these studies have focused their attention in single-objective GP, with just a few exceptions where Pareto-based dominance algorithms such as NSGA-II and SPEA2 have been used as frameworks to test whether highly popular semantics-based methods, such as Semantic Similarity-based Crossover (SSC), helps or hinders evolutionary search. Surprisingly it has been reported that the benefits exhibited by SSC in SOGP are not seen in Pareto-based dominance Multi-objective GP. In this work, we are interested in studying if the same carries out in Multi-objective Evolutionary Algorithms based on Decomposition (MOEA/D). By using the MNIST dataset, a well-known dataset used in the machine learning community, we show how SSC in MOEA/D promotes semantic diversity yielding better results compared to when this is not present in canonical MOEA/D.
