Authors:
Jiří Kubalík
1
and
Robert Babuška
2
Affiliations:
1
Czech Institute of Informatics, Robotics, and Cybernetics and CTU in Prague, Czech Republic
;
2
Czech Institute of Informatics, Robotics, and Cybernetics, CTU in Prague and Delft University of Technology, Czech Republic
Keyword(s):
Genetic Programming, Single Node Genetic Programming, Symbolic Regression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
This paper presents a first step of our research on designing an effective and efficient GP-based method for
solving the symbolic regression. We have proposed three extensions of the standard Single Node GP, namely
(1) a selection strategy for choosing nodes to be mutated based on the depth of the nodes, (2) operators
for placing a compact version of the best tree to the beginning and to the end of the population, and (3) a
local search strategy with multiple mutations applied in each iteration. All the proposed modifications have
been experimentally evaluated on three symbolic regression problems and compared with standard GP and
SNGP. The achieved results are promising showing the potential of the proposed modifications to significantly
improve the performance of the SNGP algorithm.