Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization
<|reference_start|>Benchmarking Parameter Control Methods in Differential Evolution for Mixed-Integer Black-Box Optimization: Differential evolution (DE) generally requires parameter control methods (PCMs) for the scale factor and crossover rate. Although a better understanding of PCMs provides a useful clue to designing an efficient DE, their effectiveness is poorly understood in mixed-integer black-box optimization. In this context, this paper benchmarks PCMs in DE on the mixed-integer black-box optimization benchmarking function (bbob-mixint) suite in a component-wise manner. First, we demonstrate that the best PCM significantly depends on the combination of the mutation strategy and repair method. Although the PCM of SHADE is state-of-the-art for numerical black-box optimization, our results show its poor performance for mixed-integer black-box optimization. In contrast, our results show that some simple PCMs (e.g., the PCM of CoDE) perform the best in most cases. Then, we demonstrate that a DE with a suitable PCM performs significantly better than CMA-ES with integer handling for larger budgets of function evaluations. Finally, we show how the adaptation in the PCM of SHADE fails.<|reference_end|>
@article{tanabe2024benchmarking,
    title={Benchmarking Parameter Control Methods in Differential Evolution for
  Mixed-Integer Black-Box Optimization},
    author={Ryoji Tanabe},
    journal={arXiv preprint arXiv:2404.03303},
    year={2024},
    doi={10.1145/3638529.3654019},
    archivePrefix={arXiv},
    eprint={2404.03303},
    primaryClass={cs.NE}
}