Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms

•Automated design of Genetic Programming classification algorithms is presented.•Automated design uses a genetic algorithm and grammatical evolution.•The approach is trained and tested using real-world binary and multi-class data.•Grammatical evolution designed classifiers perform better for binary...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Expert systems with applications Ročník 104; s. 213 - 234
Hlavní autori: Nyathi, Thambo, Pillay, Nelishia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 15.08.2018
Elsevier BV
Predmet:
Artificial intelligence
Automated design
Classification
Classifiers
Decision trees
Evolutionary algorithms
Genetic algorithm
Genetic algorithms
Genetic programming
Grammatical evolution
Matrix
Programming
Syntax
Grammatical evolution
Genetic algorithm
Genetic programming
Automated design
Classification
ISSN:0957-4174, 1873-6793
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:•Automated design of Genetic Programming classification algorithms is presented.•Automated design uses a genetic algorithm and grammatical evolution.•The approach is trained and tested using real-world binary and multi-class data.•Grammatical evolution designed classifiers perform better for binary classification.•Genetic algorithm designed classifiers perform better for multi-classification. Genetic Programming (GP) is gaining increased attention as an effective method for inducing classifiers for data classification. However, the manual design of a genetic programming classification algorithm is a non-trivial time consuming process. This research investigates the hypothesis that automating the design of a GP classification algorithm for data classification can still lead to the induction of effective classifiers and also reduce the design time. Two evolutionary algorithms, namely, a genetic algorithm (GA) and grammatical evolution (GE) are used to automate the design of GP classification algorithms. The classification performance of the automated designed GP classifiers i.e. GA designed GP classifiers and GE designed GP classifiers are compared to each other and to manually designed GP classifiers on real-world problems. Furthermore, a comparison of the design times of automated design and manual design is also carried out for the same set of problems. The automated designed classifiers were found to outperform manually designed classifiers across problem domains. Automated design time is also found to be less than manual design time. This study revealed that for the considered datasets GE performs better for binary classification while the GA does better for multiclass classification. Overall the results of the study are in support of the hypothesis.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.03.030