Fitness approximation methods have been widely used to solve complex benchmark problems and real-world applications. Current approaches of fitness approximation can be categorized into two types: fitness inheritance and surrogates. Surrogate model adopts a computationally less expensive model for fitness function, while fitness inheritance approximates the fitness on the basis of parent’s fitness. There is a trade-off between model complexity and model fidelity. Though fitness inheritance is computationally cheaper than surrogates due to the lack of modeling process, surrogates can have higher fidelity than fitness inheritance. This study utilizes fitness inheritance to address the key issues in the design of fitness approximation, i.e., cold start, model management, model selection, evolution control and fidelity-complexity trade-off. Specifically, three frameworks are proposed in the present study, i.e., fitness inheritance aided surrogates (FIS) framework, active learning based fitness inheritance (ALFI), and evolution of biocoenosis through symbiosis with fitness approximation (EBSFA). First, the FIS framework presents early modeling, collaboration of fitness inheritance and surrogates, and inheritance-based model management criterion to tackle the cold start, model selection, and model management issues, respectively. Empirical studies verify that FIS framework is effective and efficient in comparison to a state-of-the-art surrogate-assisted memetic algorithm (SAMA) on CEC 2014 test problems. Nonetheless, FIS framework still considers the use of surrogates which becomes a bottleneck of efficiency. To address this issue, the proposed ALFI ponders fitness inheritance with ensemble which integrates fitness inheritance with different distance measures for generating an effective and efficient approximation model. Introducing the concept of active learning in the ALFI broadens the view of evolution control. Empirical results validate that ALFI framework has effectiveness and efficiency, in comparison to SAMA and FIS framework. Beyond single task optimization, extending the use of fitness approximation on evolutionary multitasking leads to the EBSFA. Inspiring from symbiosis in biocoenosis, the EBSFA evolves several EAs concurrently as biocoenosis and exchanges information among EAs as symbiosis. The selection of candidate individuals to be evaluated among a concatenate offspring is taken by the ensemble of multiple fitness approximation derived from different tasks. In addition, this study proposes a set of new benchmark problems for evolutionary many-tasking (MaTPs) to validate the performance. Experimental results exhibit the effectiveness of the proposed EBSFA framework over a state-of-the-art numerical optimization method, the covariance matrix adaptation evolution strategy (CMAES), a well-known evolutionary multitasking method, the multi-factorial evolutionary algorithm (MFEA), and the ALFI framework on the four MaTPs. The EBSFA also outperforms a state-of-the-art evolutionary multitasking method on the benchmark problems of CEC 2017 competition for multitask optimization.