Amanote Research
Register
Sign In
Data-Efficient Neuroevolution With Kernel-Based Surrogate Models
doi 10.1145/3205455.3205510
Full Text
Open PDF
Abstract
Available in
full text
Date
January 1, 2018
Authors
Adam Gaier
Alexander Asteroth
Jean-Baptiste Mouret
Publisher
ACM Press
Related search
Efficient Global Multidisciplinary Optimization Based on Surrogate Models
Efficient Surrogate Modeling Methods for Large-Scale Earth System Models Based on Machine-Learning Techniques
Geoscientific Model Development
Earth
Simulation
Planetary Sciences
Modeling
Predicting Noisy Data With an Improvement RBF Neural Network for Surrogate Models
Model-Based Kernel for Efficient Time Series Analysis
Computationally Efficient Surrogate Response Models for Mechanistic–empirical Pavement Analysis and Design
Structure and Infrastructure Engineering
Building
Geotechnical Engineering
Risk
Mechanical Engineering
Civil
Ocean Engineering
Engineering Geology
Construction
Safety
Structural Engineering
Reliability
Quality
Optimizing the Cloud Data Center Availability Empowered by Surrogate Models
Performance of Surrogate Models in Reliability-Based Design Optimization
Efficient Reduction of Support Vectors in Kernel-Based Methods
Operator-Valued Kernel-Based Vector Autoregressive Models for Network Inference
Machine Learning
Artificial Intelligence
Software
