Memetic computing is a blooming research area, which treats memes as the fundamental building blocks of information transfer. Evolutionary multitasking is an emerging topic in memetic computation, which applies evolutionary algorithm to optimize multiple tasks at a time. A famous class of algorithms for evolutionary multitasking is the multi-factorial evolutionary algorithm (MFEA). Nevertheless, current MFEAs only consider problems with small number of tasks, resulting in a lack of effective information transfer strategy. This study proposes a framework for evolutionary multitasking, called the evolution of biocoenosis through symbiosis with fitness approximation (EBSFA). The EBSFA incorporates evolution of biocoenosis through symbiosis (EBS) with fitness approximation to ameliorate the information transfer. The improvement of EBSFA is three-fold, including (1) the adaptive control of information transfer among tasks, (2) the selection of individuals from the universal offspring pool for evaluation based on fitness approximation, and (3) an ensemble method for improving the accuracy of fitness approximation through k nearest neighbors. Experimental analysis verifies the effectiveness and efficiency of the proposed EBSFA, by comparison with an advanced single-tasking method, the covariance matrix adaptation evolution strategy (CMAES), an illustrious multitasking optimization method, the MFEA-II, and an evolutionary many-tasking method, the EBS on a set of many-tasking benchmark problems. The results show that EBSFA can gain nice solution quality and fast convergence speed. Further analysis validates the effectiveness of the proposed components on improving the information transfer.

模因计算是一个蓬勃发展的研究领域，它将模因视为信息传输的基本组成部分。进化多任务处理是模因计算中的一个新兴主题，它应用进化算法来一次优化多个任务。进化多任务处理的一类著名算法是多因子进化算法（MFEA）。但是，当前的MFEA仅考虑任务数量少的问题，从而导致缺乏有效的信息传输策略。这项研究提出了一种进化多任务的框架，称为通过适应性共生（EBSFA）通过共生进化的生物群落。EBSFA结合了通过共生（EBS）进行的生物群落进化和适应度近似，以改善信息传递。EBSFA的改进有三方面，k个最近的邻居。通过与先进的单任务方法，协方差矩阵适应进化策略（CMAES），出色的多任务优化方法，MFEA-II和进化多任务方法进行比较，实验分析验证了所提出的EBSFA的有效性和效率。 ，EBS面临一系列多任务基准测试问题。结果表明，EBSFA具有良好的求解质量和较快的收敛速度。进一步的分析验证了提出的组件在改善信息传递方面的有效性。