Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Expensive optimization problem (EOP) widely exists in various significant real-world
applications. However, EOP requires expensive or even unaffordable costs for evaluating …

Learning and optimization are the two essential abilities of human beings for problem
solving. Similarly, computer scientists have made great efforts to design artificial neural …

As an advanced artificial intelligence technique for solving learning problems, deep learning
(DL) has achieved great success in many real-world applications and attracted increasing …

With the enormous increase in energy usage by cloud data centers for handling various
workflow applications, the energy-aware cloud workflow scheduling has become a hot …

Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

In recent years, the multi-task learning for knowledge graph-based recommender system,
termed MKR, has shown its promising performance and has attracted increasing interest …

Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple
populations for cooperatively solving complex optimization problems. However, how to …

Plant diseases pose a major challenge in the agricultural sector, which affects plant
development and crop productivity. Sugarcane farming is a highly organized part of farming …

As transportation system plays a vastly important role in combatting newly-emerging and
severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing …