Evolutionary transfer optimization (ETO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the …
Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature sel…
Evolutionary large-scale multiobjective optimization (ELMO) has received increasing attention in recent years. This study has compared various existing optimizers for ELMO on different benchmarks, …
When solving constrained multiobjective optimization problems (CMOPs), the utilization of infeasible solutions significantly affects algorithm’s performance because they not only maintain diversi…
Multiobjective multitask optimization (MMO) attempts to solve several problems simultaneously. This is commonly done by identifying useful knowledge to transfer between tasks, thereby producing opt…
Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimiz…
Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and…
Intensity-modulated radiotherapy (IMRT) is one of the most popular techniques for cancer treatment. However, existing IMRT planning methods can only generate one solution at a time and, consequentl…
Evolutionary sequential transfer optimization is a paradigm that leverages search experience from solved source optimization tasks to accelerate the evolutionary search of a target task. Even thoug…
Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the existing KT methods still face two challenges. First, the tasks may commonly have different dimensionalities…