Recently, evolutionary multitasking has been employed to generate a ``set of Pareto sets" (SOS) for machine learning models, addressing diverse task settings across heterogeneous environments. This involves creating a repository of compact, specialized solution models that are collectively tailored to each specific task setting and environment, enabling users to select the most suitable model based on particular specifications and preferences. In this paper, we further demonstrate the versatility and applicability of the SOS concept across diverse domains, focusing on three real-world problems: engineering design problems, inventory management problems, and hyperparameter optimization problems. Additionally, as evolutionary multitasking has proven effective in generating the SOS, we investigate the performance of current evolutionary multitasking methods on these real-world problems. Subsequently, we present visualizations of the generated SOS in both decision and objective spaces, complemented by the development of a measurement to gauge the similarity between different Pareto sets corresponding to diverse tasks. Finally, we show that by systematically examining the shifts in Pareto optimal designs across different task settings though the SOS solutions, users can gain deeper understandings on the dynamic interplay between design solutions and their performance in different settings or contexts.

翻译：近期，进化多任务处理技术被用于生成机器学习模型的"Pareto解集集合"，以应对异构环境中的多样化任务设置。该方法构建了一个包含紧凑型专用解模型的资源库，这些模型针对每个特定任务设置和环境进行集体定制，使用户能够根据具体规格和偏好选择最合适的模型。本文进一步展示了Pareto解集集合概念在多个领域中的通用性和适用性，重点聚焦于三个现实问题：工程设计问题、库存管理问题以及超参数优化问题。此外，鉴于进化多任务处理技术已被证明能有效生成Pareto解集集合，本文考察了当前进化多任务处理方法在这些现实问题上的表现。随后，我们在决策空间和目标空间中对生成的Pareto解集集合进行了可视化呈现，并开发了一种度量方法以评估不同任务对应的Pareto解集之间的相似性。最后，研究表明，通过Pareto解集集合系统地分析不同任务设置中Pareto最优设计的演变规律，用户可更深入地理解设计方案在不同情境中的动态交互关系及其性能表现。