Lexicase-based Selection Methods with Down-sampling for Symbolic Regression Problems: Overview and Benchmark
<|reference_start|>Lexicase-based Selection Methods with Down-sampling for Symbolic Regression Problems: Overview and Benchmark: In recent years, several new lexicase-based selection variants have emerged due to the success of standard lexicase selection in various application domains. For symbolic regression problems, variants that use an epsilon-threshold or batches of training cases, among others, have led to performance improvements. Lately, especially variants that combine lexicase selection and down-sampling strategies have received a lot of attention. This paper evaluates random as well as informed down-sampling in combination with the relevant lexicase-based selection methods on a wide range of symbolic regression problems. In contrast to most work, we not only compare the methods over a given evaluation budget, but also over a given time as time is usually limited in practice. We find that for a given evaluation budget, epsilon-lexicase selection in combination with random or informed down-sampling outperforms all other methods. Only for a rather long running time of 24h, the best performing method is tournament selection in combination with informed down-sampling. If the given running time is very short, lexicase variants using batches of training cases perform best.<|reference_end|>
@article{geiger2024lexicase-based,
    title={Lexicase-based Selection Methods with Down-sampling for Symbolic
  Regression Problems: Overview and Benchmark},
    author={Alina Geiger, Dominik Sobania, Franz Rothlauf},
    journal={arXiv preprint arXiv:2407.21632},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21632},
    primaryClass={cs.NE}
}