Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

The evolutionary algorithms (EAs) have been shown favorable performance for feature
selection. However, a large number of evaluations are required through the EAs. Thus, they …

Abstract Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of
optimisers developed to undertake problems with computationally expensive fitness …

With the recent advances of Deep Neural Networks (DNNs) in real-world applications, such
as Automated Driving Systems (ADS) for self-driving cars, ensuring the reliability and safety …

Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO)
invests a lot of computation time to generate the candidate solution that best balances the …

Bayesian optimization (BO) is a powerful method for solving expensive black-box
optimization problems, and it determines the candidate solutions for expensive evaluation …

Fitness functions of real-world optimization problems often need to be analyzed by
expensive experiments or numerical simulations. Integrating these expensive simulations or …

Computationally expensive optimization problems are difficult for an evolutionary algorithm
within limited fitness evaluations, especially for many-objective optimization. To remedy this …

Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for
solving expensive optimization problems (EOPs) whose true evaluations are …

The computationally expensive dynamic optimization problems (CEDOPs) arise when the
objective function of a dynamic optimization problem is prohibitively expensive, or even …