  The goal of transfer learning (TL) is providing a framework for exploiting
acquired knowledge from source to target data. Transfer learning approaches
compared to traditional machine learning approaches are capable of modeling
better data patterns from the current domain. However, vanilla TL needs
performance improvements by using computational intelligence-based TL. This
paper studies computational intelligence-based transfer learning techniques and
categorizes them into neural network-based, evolutionary algorithm-based, swarm
intelligence-based and fuzzy logic-based transfer learning.
