We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.
翻译:我们对两种单一目标和两种多目标的全球优化进化算法作为一个算法配置问题进行了全面的全球敏感性分析。也就是说,我们调查超参数对算法性能的直接影响和与其他超参数的相互作用影响的质量。我们使用三种敏感度分析方法(Morris LHS、Morris和Sobol)系统分析共变矩阵适应演进战略、差异演进、非主流分类遗传算法III和基于分解的多目标进化算法,这个框架揭示了超参数对采样方法和性能衡量标准的行为。这就是,它回答了诸如超参数影响模式、互动方式、互动程度及其直接影响程度等问题。因此,超参数的排序显示了它们的调控顺序,影响模式揭示了算法的稳定性。