TY - GEN
T1 - Complexity Measures for Multi-Objective Symbolic Regression
AU - Kommenda, Michael
AU - Beham, Andreas
AU - Affenzeller, Michael
AU - Kronberger, Gabriel
PY - 2015
Y1 - 2015
N2 - Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified a-priori. The result of the optimization is not a single solution anymore, but a whole Pareto-front describing the trade-off between accuracy and complexity.
In this contribution we study which complexity measures are most appropriately used in symbolic regression when performing multi-objective optimization with NSGA-II. Furthermore, we present a novel complexity measure that includes semantic information based on the function symbols occurring in the models and test its effects on several benchmark datasets. Results comparing multiple complexity measures are presented in terms of the achieved accuracy and model length to illustrate how the search direction of the algorithm is affected.
AB - Multi-objective symbolic regression has the advantage that while the accuracy of the learned models is maximized, the complexity is automatically adapted and need not be specified a-priori. The result of the optimization is not a single solution anymore, but a whole Pareto-front describing the trade-off between accuracy and complexity.
In this contribution we study which complexity measures are most appropriately used in symbolic regression when performing multi-objective optimization with NSGA-II. Furthermore, we present a novel complexity measure that includes semantic information based on the function symbols occurring in the models and test its effects on several benchmark datasets. Results comparing multiple complexity measures are presented in terms of the achieved accuracy and model length to illustrate how the search direction of the algorithm is affected.
UR - https://www.scopus.com/pages/publications/84952333216
U2 - 10.1007/978-3-319-27340-2_51
DO - 10.1007/978-3-319-27340-2_51
M3 - Conference proceedings
SN - 978-3-319-27339-6
VL - 9520
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 409
EP - 416
BT - Computer Aided Systems Theory – EUROCAST 2015. 15th International Conference, Las Palmas de Gran Canaria, Spain, February 8-13, 2015, Revised Selected Papers
A2 - Pichler, Franz
A2 - Moreno-Díaz, Roberto
A2 - Quesada-Arencibia, Alexis
PB - Springer
ER -