Evolutionary multitask optimization (EMTO) is a newly emerging research area in the field of evolutionary computation. It investigates how to solve multiple optimization problems (tasks) at the same time via evolutionary algorithms (EAs) to improve on the performance of solving each task independently, assuming if some component tasks are related then the useful knowledge (e.g., promising candidate solutions) acquired during the process of solving one task may assist in (and also benefit from) solving the other tasks. In EMTO, task relatedness is typically unknown in advance and needs to be captured via EA’s population. Since the population of an EA can only cover a subregion of the solution space and keeps evolving during the search, thus captured task relatedness is local and dynamic. The multifactorial EA (MFEA) is one of the most representative EMTO techniques, inspired by the bio-cultural model of multifactorial inheritance, which transmits both biological and cultural traits from the parents to the offspring. MFEA has succeeded in solving various multitask optimization (MTO) problems. However, the intensity of knowledge transfer in MFEA is determined via its algorithmic configuration without considering the degree of task relatedness, which may prevent the effective sharing and utilization of the useful knowledge acquired in related tasks. To address this issue, we propose a self-regulated EMTO (SREMTO) algorithm to automatically adapt the intensity of cross-task knowledge transfer to different and varying degrees of relatedness between different tasks as the search proceeds so that the useful knowledge in common for solving related tasks can be captured, shared, and utilized to a great extent. We compare SREMTO with MFEA and its variants as well as the single-task optimization counterpart of SREMTO on two MTO test suites, which demonstrates the superiority of SREMTO.

中文翻译：

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自调节进化多任务优化

进化多任务优化（EMTO）是进化计算领域一个新兴的研究领域。它研究如何通过进化算法 (EA) 同时解决多个优化问题（任务）以提高独立解决每个任务的性能，假设某些组件任务是否相关，那么有用的知识（例如，有前途的候选解决方案）在解决一项任务的过程中获得的知识可能有助于（并从中受益）解决其他任务。在 EMTO 中，任务相关性通常是事先未知的，需要通过 EA 的总体来捕获。由于 EA 的种群只能覆盖解空间的一个子区域并在搜索过程中不断演化，因此捕获的任务相关性是局部的和动态的。多因子 EA (MFEA) 是最具代表性的 EMTO 技术之一，其灵感来自多因子遗传的生物文化模型，它将生物和文化特征从父母传给后代。MFEA 已成功解决各种多任务优化 (MTO) 问题。然而，MFEA中知识转移的强度是由其算法配置决定的，而没有考虑任务相关程度，这可能会阻碍相关任务中获得的有用知识的有效共享和利用。为了解决这个问题，我们提出了一种自我调节的 EMTO（SREMTO）算法，随着搜索的进行，自动调整跨任务知识转移的强度，以适应不同任务之间不同程度的相关性，以便可以捕获解决相关任务的共同有用知识，共享，并在很大程度上利用。我们在两个 MTO 测试套件上将 SREMTO 与 MFEA 及其变体以及 SREMTO 的单任务优化对应物进行了比较，这证明了 SREMTO 的优越性。