Recent advances in Bayesian optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization
Solving constrained optimization problems (COPs) with evolutionary algorithms (EAs) is a
popular research direction due to its potential and diverse applications. One of the key …
popular research direction due to its potential and diverse applications. One of the key …
A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems
Evolutionary algorithms require large number of function evaluations to locate the global
optimum, making it computationally prohibitive on dealing with expensive problems …
optimum, making it computationally prohibitive on dealing with expensive problems …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
Adaptive auxiliary task selection for multitasking-assisted constrained multi-objective optimization [feature]
Solving constrained multi-objective optimization problems (CMOPs) is one of the most
popular research topics in the multi-objective optimization community. Various approaches …
popular research topics in the multi-objective optimization community. Various approaches …
Multiple classifiers-assisted evolutionary algorithm based on decomposition for high-dimensional multiobjective problems
T Sonoda, M Nakata - IEEE Transactions on Evolutionary …, 2022 - ieeexplore.ieee.org
Surrogate-assisted multiobjective evolutionary algorithms (MOEAs) have advanced the field
of computationally expensive optimization, but their progress is often restricted to low …
of computationally expensive optimization, but their progress is often restricted to low …
Large language model for multi-objective evolutionary optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization
Multi-objective optimization problems in many real-world applications are characterized by
computationally or economically expensive objectives, which cannot provide sufficient …
computationally or economically expensive objectives, which cannot provide sufficient …
A classification surrogate-assisted multi-objective evolutionary algorithm for expensive optimization
J Li, P Wang, H Dong, J Shen, C Chen - Knowledge-Based Systems, 2022 - Elsevier
Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been
developed for solving expensive optimization problems. According to the roles that the …
developed for solving expensive optimization problems. According to the roles that the …
Expensive optimization via surrogate-assisted and model-free evolutionary optimization
The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient
approaches for solving expensive optimization problems. However, it still faces challenges …
approaches for solving expensive optimization problems. However, it still faces challenges …