Improving the Parsimony of Regression Models for an Enhanced Genetic Programming Process

被引：0
|
作者：
Zavoianu, Alexandru-Ciprian [1 ]
Kronberger, Gabriel [2 ]
Kommenda, Michael [2 ]
Zaharie, Daniela [1 ]
Affenzeller, Michael [2 ]
机构：
[1] West Univ Timisoara, Dept Comp Sci, Timisoara, Romania
[2] Upper Austrian Univ Appl Sci, Heurist & Evolutionary Algorithms Lab, Vienna, Austria
来源：
COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2011, PT I | 2012年 / 6927卷
关键词：
genetic programming; symbolic regression; solution parsimony; bloat control;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by similar or equal to 40% the average GP solution parsimony without impacting average solution accuracy.
引用
收藏
页码：264 / 271
页数：8
相关论文
共 50 条
  • [1] On improving genetic programming for symbolic regression
    Gustafson, S
    Burke, EK
    Krasnogor, N
    [J]. 2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, : 912 - 919
  • [2] Parsimony Measures in Multi-objective Genetic Programming for Symbolic Regression
    Burlacu, Bogdan
    Kronberger, Gabriel
    Kommenda, Michael
    Affenzeller, Michael
    [J]. PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION), 2019, : 338 - 339
  • [3] Balancing Accuracy and Parsimony in Genetic Programming
    Zhang, Byoung-Tak
    Muehlenbein, Heinz
    [J]. EVOLUTIONARY COMPUTATION, 1995, 3 (01) : 17 - 38
  • [4] Genetic programming, validation sets, and parsimony pressure
    Gagne, Christian
    Schoenauer, Marc
    Parizeau, Marc
    Tomassini, Marco
    [J]. GENETIC PROGRAMMING, PROCEEDINGS, 2006, 3905 : 109 - 120
  • [5] Genetic Programming for Symbolic Regression of Chemical Process Systems
    Babu, B. V.
    Karthik, S.
    [J]. ENGINEERING LETTERS, 2007, 14 (02)
  • [6] Improving Generalisation of Genetic Programming for Symbolic Regression with Structural Risk Minimisation
    Chen, Qi
    Xue, Bing
    Shang, Lin
    Zhang, Mengjie
    [J]. GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, : 709 - 716
  • [7] Using Multiobjective Genetic Programming to infer logistic polynomial regression models
    Hunter, A
    [J]. ECAI 2002: 15TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2002, 77 : 193 - 197
  • [8] Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions
    Virgolin, M.
    Alderliesten, T.
    Witteveen, C.
    Bosman, P. A. N.
    [J]. EVOLUTIONARY COMPUTATION, 2021, 29 (02) : 211 - 237
  • [9] Improving Genetic Programming Based Symbolic Regression Using Deterministic Machine Learning
    Icke, Ilknur
    Bongard, Joshua C.
    [J]. 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2013, : 1763 - 1770
  • [10] Evolution of Covariance Functions for Gaussian Process Regression Using Genetic Programming
    Kronberger, Gabriel
    Kommenda, Michael
    [J]. COMPUTER AIDED SYSTEMS THEORY, PT 1, 2013, 8111 : 308 - 315
12345