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A Surrogate-Assisted Highly Cooperative Coevolutionary Algorithm for Hyperparameter Optimization in Deep Convolutional Neural Network
Convolutional neural networks (CNNs) have gained remarkable success in recent years.
Surrogate-assisted cooperative signal optimization for large-scale traffic networks
The decomposition operation significantly narrows the search space of the whole traffic network, and the surrogate-assisted optimizer greatly lowers the computational burden by reducing the number of expensive traffic simulations.
Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection
By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems.
A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems
SVG first designs a general-separability-oriented detection criterion according to whether the optimum of a variable changes with other variables.
An Eigenspace Divide-and-Conquer Approach for Large-Scale Optimization
Different from existing DC-based algorithms that perform decomposition and optimization in the original decision space, EDC first establishes an eigenspace by conducting singular value decomposition on a set of high-quality solutions selected from recent generations.
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
As for the nonseparable sub-problems, the surrogate models are employed to evaluate the corresponding sub-solutions, and the original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models.
Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering
As a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
On the one hand, by reducing the sensitivity of the indicator in DG to the roundoff error and the magnitude of contribution weight of subcomponent, we proposed a new indicator for two variables which is much more sensitive to their interaction.
Surrogate Model Assisted Cooperative Coevolution for Large Scale Optimization
It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a divide-and-conquer strategy.
Boosting Cooperative Coevolution for Large Scale Optimization with a Fine-Grained Computation Resource Allocation Strategy
Cooperative coevolution (CC) has shown great potential in solving large scale optimization problems (LSOPs).
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive
This study first systematically analyses the reasons for the deficiency of the traditional GEDA, then tries to enhance its performance by exploiting its evolution direction, and finally develops a new GEDA variant named EDA2.
