Interaction-Transformation Evolutionary Algorithm for Symbolic Regression
About
The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithm capable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm.
Fabricio Olivetti de Franca, Guilherme Seidyo Imai Aldeia• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Symbolic Regression | SRBench black-box (test) | R^20.6407 | 53 | |
| Symbolic Regression | SRBench known solutions 0.1% noise | Symbolic Solution Rate13.77 | 18 | |
| Symbolic Regression | SRBench known solutions 1% noise | Symbolic Solution Rate7.69 | 18 | |
| Symbolic Regression | SRBench known solutions 10% noise | Symbolic Solution Rate1.46 | 18 | |
| Symbolic Regression | SRBench known solutions 0.0% noise | Solution Rate20.77 | 18 | |
| Symbolic Regression | Feynman Problem II.34.29b | Mean R2 Score1 | 15 | |
| Symbolic Regression | Feynman Problem III.4.32 | Mean R2 Score100 | 15 | |
| Symbolic Regression | Feynman Problem II.24.17 | Mean R21 | 15 | |
| Symbolic Regression | Feynman Problem II.34.11 | Mean R21 | 15 | |
| Symbolic Regression | Feynman Problem II.38.3 | Mean R2 Score1 | 15 |
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