An Evolutionary Computation Based Model for Testing Transfer Learning Strategies

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

“…In other words, the goal is to find a mathematical expression that is a good approximation for the real function relating to the variables of interest. The GA(S) and GA(T) 25 algorithms aim to test TL techniques in frameworks based on Genetic Algorithms. In this experiment, the authors created an environment with source and target problems to test various estimations, such as distances and population numbers generated by the methods.…”

“…Instance Weighting Genetic Algorithm Alghamdi et al 25 Genetic Programming Chen et al 26 Feature Selection-Based Domain Adaptation…”

“…In Instance Weighting, three algorithms proposed in the base studies are Genetic Algorithm for Source Task Problems -GA(S), 25 Genetic Algorithm for Target Task Problems -GA(T), 25 and Instance Transfer Genetic Programming -ITGP. 26 Instance Weighting, a statistical bias correction technique, addresses sample selection bias, playing a crucial role in transfer learning to overcome the disparity between source and target domains.…”

“…Among the evolutionary computing algorithms discussed in the foundational studies, there are two approaches that use Genetic Algorithms (GA) 25 to assist in Transfer Learning techniques, and four that use Genetic Programming (GP). 26,[31][32][33] Both GA(S) and GA(T) 25 apply GA to model the TL process as a search problem, allowing them to control and measure various factors affecting transfer performance, such as problem difficulty, problem similarity, and information transfer methods. The goal of GA(S) and GA(T) is to understand the underlying mechanisms and principles of TL, aiming to develop more effective knowledge transfer techniques.…”