Complexity Measures for Multi-objective Symbolic Regression

Understand

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.