In the literature of mitigating unfairness in machine learning (ML), many fairness measures are designed to evaluate predictions of learning models and also utilized to guide the training of fair m…
In practical multicriterion decision making, it is cumbersome if a decision maker (DM) is asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal front. This is a par…
Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpo…
Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep learning, especially …
Reinforcement learning (RL) algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalization ability of RL algorithms. The Ge…
Many real-world optimization problems are dynamic. The field of robust optimization over time (ROOT) deals with dynamic optimization problems in which frequent changes of the deployed solution are …
The uncertain capacitated arc routing problem (UCARP) is an NP-hard combinatorial optimization problem with a wide range of applications in logistics domains. Genetic programming (GP) hyper-heurist…
Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research e…
The capacitated arc routing problem (CARP) is a challenging combinatorial optimization problem abstracted from many real-world applications, such as waste collection, road gritting, and mail delive…
Knee points, characterized as a small improvement on one objective can lead to a significant degradation on at least one of the other objectives, are attractive to decision makers (DMs) in multicri…