On the Use of Semantics in Multi-objective Genetic Programming

被引：12
|
作者：
Galvan-Lopez, Edgar [1 ]
Mezura-Montes, Efren [2 ]
ElHara, Ouassim Ait [3 ,4 ,5 ]
Schoenauer, Marc [3 ,4 ,5 ]
机构：
[1] Trinity Coll Dublin, Sch Comp Sci & Stat, Dublin, Ireland
[2] Univ Veracruzana, Xalapa, Veracruz, Mexico
[3] INRIA, TAO, Orsay, France
[4] CNRS, LRI, Orsay, France
[5] Univ Paris Saclay, U Paris Sud, Orsay, France
来源：
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIV | 2016年 / 9921卷
关键词：
CROSSOVER; ROLES;
D O I：
10.1007/978-3-319-45823-6_33
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Research on semantics in Genetic Programming (GP) has increased dramatically over the last number of years. Results in this area clearly indicate that its use in GP can considerably increase GP performance. Motivated by these results, this paper investigates for the first time the use of Semantics in Muti-objective GP within the well-known NSGA-II algorithm. To this end, we propose two forms of incorporating semantics into a MOGP system. Results on challenging (highly) unbalanced binary classification tasks indicate that the adoption of semantics in MOGP is beneficial, in particular when a semantic distance is incorporated into the core of NSGA-II.
引用
收藏
页码：353 / 363
页数：11
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