Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure
This work addresses runtime performance issues in genetic programming for symbolic regression, though it is incremental as it builds on existing algorithms.
The authors tackled the problem of slow diversity measurement in genetic programming by introducing a fast hash-based tree distance measure, which when combined with GA and NSGA-II algorithms, consistently outperformed standard methods on symbolic regression benchmarks.
Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.