Phenotypic Duplication and Inversion in Cartesian Genetic Programming applied to Boolean Function Learning

被引:2
作者
Kalkreuth, Roman [1 ]
机构
[1] TU Dortmund Univ, Dept Comp Sci, Dortmund, North Rhine Wes, Germany
来源
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022 | 2022年
关键词
Cartesian Genetic Programming; Mutation; Duplication; Inversion; Boolean Function Learning;
D O I
10.1145/3520304.3529065
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The search performance of Cartesian Genetic Programming (CGP) relies to a large extent on the sole use of genotypic point mutation in combination with extremely large redundant genotypes. Over the last years, steps have been taken to extend CGP's variation mechanisms by the introduction of advanced methods for recombination and mutation. One branch of these contributions addresses phenotypic variation in CGP. However, recent studies have demonstrated the limitations of phenotypic recombination in Boolean function learning and highlighted the effectiveness of the mutationonly approach. Therefore, in this work, we further explore phenotypic mutation in CGP by the introduction and evaluation of two phenotypic mutation operators that are inspired by chromosomal rearrangement. Our initial findings show that the proposed methods can significantly improve the search performance of CGP on various single- and multiple-output Boolean function benchmarks.
引用
收藏
页码:566 / 569
页数:4
相关论文
共 11 条
[1]  
Ebner M, 2012, COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS III, P69
[2]  
Holland J.H., 1975, ADAPTATION NATURAL A, P1
[3]   A Comparative Study on Crossover in Cartesian Genetic Programming [J].
Husa, Jakub ;
Kalkreuth, Roman .
GENETIC PROGRAMMING (EUROGP 2018), 2018, 10781 :203-219
[4]   A Comprehensive Study on Subgraph Crossover in Cartesian Genetic Programming [J].
Kalkreuth, Roman .
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE (IJCCI), 2020, :59-70
[5]   A New Subgraph Crossover for Cartesian Genetic Programming [J].
Kalkreuth, Roman ;
Rudolph, Gunter ;
Droschinsky, Andre .
GENETIC PROGRAMMING, EUROGP 2017, 2017, 10196 :294-310
[6]  
Kalkreuth Roman Tobias, 2021, THESIS, DOI [10.17877/DE290R-22504, DOI 10.17877/DE290R-22504]
[7]   Genetic algorithms for the travelling salesman problem:: A review of representations and operators [J].
Larrañaga, P ;
Kuijpers, CMH ;
Murga, RH ;
Inza, I ;
Dizdarevic, S .
ARTIFICIAL INTELLIGENCE REVIEW, 1999, 13 (02) :129-170
[8]  
Miller J., 2003, CARTESIAN GENETIC PR, VVolume 4, DOI [DOI 10.1007/978-3-642-17310-32, 10.1007/978-3-642-17310-3]
[9]  
Miller JF, 1999, GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, P1135
[10]  
Ryan C., 1998, Genetic Programming. First European Workshop, EuroGP'98. Proceedings, P83, DOI 10.1007/BFb0055930