On the use of evolutionary and swarm intelligence algorithms in transfer learning approaches: a review

Abstract

Transfer Learning (TL) utilizes pre-trained models to solve similar problems. The knowledge from the original model is transferred to a new model during training, aiming to leverage previous knowledge in a new task. Natural Computing (NC) algorithms, such as Evolutionary Computation (EC) and Swarm Intelligence (SI), draw inspiration from nature, adapting more easily to new computational problems. This bio-inspired adaptation can enhance the performance of TL techniques, improving generalization and reducing computational costs. We investigate how evolutionary and swarm-intelligence algorithms are applied in TL, their contributions, the addressed problems, and the conducted experiments. We employ a systematic review following the PRISMA protocol, PICOS strategy, and START software to analyze primary studies.