scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

An improved Gene Expression Programming approach for symbolic regression problems

2014 ◽  
Vol 137 ◽  
pp. 293-301 ◽  
Author(s):  
YuZhong Peng ◽  
ChangAn Yuan ◽  
Xiao Qin ◽  
JiangTao Huang ◽  
YaBing Shi
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Regression Problems

scholarly journals Hybrid Symbolic Regression with the Bison Seeker Algorithm

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Jan Merta
Keyword(s):  
Genetic Programming ◽  
Symbolic Regression ◽  
Supervised Machine Learning ◽  
Local Algorithm ◽  
Programming Approach ◽  
Model Parameters ◽  
Model Structure ◽  
Study Support ◽  
The Given ◽  
Basic Version

This paper focuses on the use of the Bison Seeker Algorithm (BSA) in a hybrid genetic programming approach for the supervised machine learning method called symbolic regression. While the basic version of symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. This paper aims to compare the basic version of symbolic regression and hybrid version with the lifetime adaptation of individuals via the Bison Seeker Algorithm. Author also investigates the influence of the Bison Seeker Algorithm on the rate of evolution in the search for function, which fits the given input-output data. The results of the current study support the fact that the local algorithm accelerates evolution, even with a few iterations of a Bison Seeker Algorithm with small populations.

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