Traditional evolution algorithms tend to start the search from scratch. However, real-world
problems seldom exist in isolation and humans effectively manage and execute multiple …

Transfer evolutionary multiobjective optimization leverages the relevant knowledge from
other source problems (distinct but possibly related) to assist the optimization of the target …

Single-objective to multi-objective/many-objective optimization (SMO) is a new paradigm in
the evolutionary transfer optimization (ETO), since there are only “1+ 4” pioneering works on …

Towards artificial general intelligence, emergent abilities of large language models (LLMs)
are observed wildly especially for well-known GPTs, which are due to scaling up primarily …

Responsible artificial general intelligence (AGI) is deeply connected with both explainable
artificial intelligence and interpretable artificial intelligence. In frontier science of evolutionary …

Recently, due to the growth in machine learning and data mining, for scheduling
applications in China's industrial intelligence, we are quite fortunate to witness a paradigm …

As a new abstract computational model in evolutionary transfer optimization (ETO), single-
objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather …

Evolutionary transfer optimization (ETO) serves as" a new frontier in evolutionary
computation research", which will avoid zero reuse of experience and knowledge from …

Multi-objective evolutionary optimization is a class of challenging and complex optimization
problems aimed at searching a set of optimal solutions in the face of multiple conflicting …

As" a new frontier in evolutionary computation research", evolutionary transfer optimization
(ETO) will overcome the traditional paradigm of zero reuse of related experience and …