Abstract
Handling catastrophic forgetting is an interesting and challenging topic in modeling the memory mechanisms of the human brain using machine learning models. From a more general point of view, catastrophic forgetting reflects the stability-plasticity dilemma, which is one of the several dilemmas to be addressed in learning systems: to retain the stored memory while learning new information. Different to the existing approaches, we introduce a Pareto-optimality based multi-objective learning framework for alleviating catastrophic learning. Compared to the single-objective learning methods, multi-objective evolutionary learning with the help of pseudorehearsal is shown to be more promising in dealing with the stability-plasticity dilemma. © 2006 IEEE.