Data-driven evolutionary optimization: An overview and case studies
Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …
constraint functions is straightforward. In solving many real-world optimization problems …
Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems
Function evaluations (FEs) of many real-world optimization problems are time or resource
consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to …
consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to …
Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey
MIE Khaldi, A Draa - Evolutionary Intelligence, 2024 - Springer
Abstract Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of
optimisers developed to undertake problems with computationally expensive fitness …
optimisers developed to undertake problems with computationally expensive fitness …
A classifier-assisted level-based learning swarm optimizer for expensive optimization
Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to
solve complex and computationally expensive optimization problems. However, most …
solve complex and computationally expensive optimization problems. However, most …
Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system
Most existing work on evolutionary optimization assumes that there are analytic functions for
evaluating the objectives and constraints. In the real world, however, the objective or …
evaluating the objectives and constraints. In the real world, however, the objective or …
Multiobjective infill criterion driven Gaussian process-assisted particle swarm optimization of high-dimensional expensive problems
Model management plays an essential role in surrogate-assisted evolutionary optimization
of expensive problems, since the strategy for selecting individuals for fitness evaluation …
of expensive problems, since the strategy for selecting individuals for fitness evaluation …
Expensive multiobjective optimization by relation learning and prediction
Expensive multiobjective optimization problems pose great challenges to evolutionary
algorithms due to their costly evaluation. Building cheap surrogate models to replace the …
algorithms due to their costly evaluation. Building cheap surrogate models to replace the …
A fast kriging-assisted evolutionary algorithm based on incremental learning
Kriging models, also known as Gaussian process models, are widely used in surrogate-
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
assisted evolutionary algorithms (SAEAs). However, the cubic time complexity of the …
Global and local surrogate-model-assisted differential evolution for waterflooding production optimization
Surrogate models, which have become a popular approach to oil‐reservoir production‐
optimization problems, use a computationally inexpensive approximation function to replace …
optimization problems, use a computationally inexpensive approximation function to replace …
Resampling methods for meta-model validation with recommendations for evolutionary computation
Meta-modeling has become a crucial tool in solving expensive optimization problems. Much
of the work in the past has focused on finding a good regression method to model the fitness …
of the work in the past has focused on finding a good regression method to model the fitness …