Multi Objective Symbolic Regression

被引：2
|
作者：
Hinde, C. J. [1 ]
Chakravorti, N. [1 ]
West, A. A. [1 ]
机构：
[1] Univ Loughborough, Loughborough, Leics, England
来源：
ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS | 2017年 / 513卷
基金：
英国工程与自然科学研究理事会;
关键词：
D O I：
10.1007/978-3-319-46562-3_31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite athletes more information and better expressions can be generated than by using single, or even double objective functions. Symbolic regression, multi-objective, non dominated sorting, genetic programming.
引用
收藏
页码：481 / 494
页数：14
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