论文标题
多目标遗传编程中的基于语义的距离方法
Semantic-based Distance Approaches in Multi-objective Genetic Programming
论文作者
论文摘要
在遗传程序（GP）背景下的语义可以理解为给定一系列输入的程序的行为，并且已在改善一系列不同问题的GP绩效方面有充分的文献记载。已经有多种不同的方法将语义纳入单目标GP。但是，多目标（MO）GP中语义的研究受到限制，本文旨在解决此问题。更具体地说，我们对MOGP中三种不同形式的语义进行了比较。一种基于语义的方法（i）基于语义相似性的交叉（SSC）是从单目标GP借用的，其中该方法始终被报告在进化搜索中有益。我们还研究了另外两种称为（II）基于语义的方法作为附加标准（SDO）和（iii）枢轴相似性SDO的方法。与规范方法和SSC相比，我们从经验和始终如一地展示了如何通过自然处理语义距离作为要在MOGP中进行优化的附加标准的方法。两种基于语义距离的方法都使用了枢轴，这是搜索空间最稀少区域的参考点，发现与该枢轴上的语义相似且不同的个体对促进多样性是有益的。此外，我们还展示了语义在单一优化中成功促进的语义如何在MOGP中采用时没有必要导致更好的性能。
Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.
