The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems

2005 ◽  
Author(s):  
R. Cavill ◽  
S. Smith ◽  
A. Tyrrell
Keyword(s):  
Evolutionary Algorithms ◽  
Symbolic Regression ◽  
Regression Problems

scholarly journals Shape-constrained Symbolic Regression – Improving Extrapolation with Prior Knowledge

2021 ◽  
pp. 1-24
Author(s):  
G. Kronberger ◽  
F. O. de Franca ◽  
B. Burlacu ◽  
C. Haider ◽  
M. Kommenda
Keyword(s):  
Evolutionary Algorithms ◽  
Prior Knowledge ◽  
Polynomial Regression ◽  
Predictive Accuracy ◽  
Symbolic Regression ◽  
Training Set ◽  
Test Set ◽  
Regression Algorithms ◽  
Selection Step ◽  
Regression Problems

Abstract We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce e.g. monotonicity of the function over selected inputs. The aim is to find models which conform to expected behaviour and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shapeconstrained symbolic regression: i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and ii) a two population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models.

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
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