Radial Basis Function Networks

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An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models
Aug 3, 2026Kei Nishihara, Yaochu Jin, Masaya NakataSurrogate Modeling Surrogate-Assisted Optimization
Organization for the Promotion of Education, Yokohama National University, Yokohama 2408501, Japan · School of Engineering, Westlake University, Hangzhou 310030, China · Faculty of Engineering, Yokohama National University, Yokohama 2408501, Japan
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
Jul 29, 2026Marc Kaufeld, Dian Zhuang, Johannes BetzCollision Avoidance Constrained Motion Planning
Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI) · School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany
Influence of Radial Basis Activation Functions on Intelligent Controller for Robotic Manipulators
Jul 2, 2026Kimmo Paldanius, Gabriel Da Silva Lima, Wallace Moreira BessaRobotic Control Robot Trajectory Tracking
Smart Systems Lab, Department of Mechanical Engineering, University of Turku, 20520, Finland.
Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics
Jun 30, 2026Amos Alwala, Gabriel da Silva Lima, Wallace Moreira BessaRobot Trajectory Tracking Feedback Control
Smart Systems, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20520 Turku, Finland.
Radial Basis Function Networks as Projection Heads in Self-Supervised Learning
Jun 19, 2026Andreas Schliebitz, Heiko Tapken, Martin AtzmuellerRepresentation Learning Self-Supervised Visual Representation Learning
Osnabrück University of Applied Sciences, Albrechtstr. 30, 49076 Osnabrück, Germany · Osnabrück University, Semantic Information Systems Group,2026 Wachsbleiche 27, 49090 Osnabrück Germany · German Research Center for Artificial Intelligence (DFKI), Hamburger Str. 24, 49084 Osnabrück, Germany
Physics-Informed Neural Networks and Radial Basis Functions for PDEs with Dirac Delta Sources
Jun 10, 2026Manuel Reyna, Alexandre TartakovskyPDE Solving Physics-Informed ML
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, 205 North Mathews Avenue, Urbana, 61801, IL, USA
Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization
Jun 3, 2026Ammar Hoori, Yuichi MotaiMetaheuristic Optimization Neural Network Optimization
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA · Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA
Hierarchical RBF-KAN and RBF-SKAN Architectures for Multidimensional Function Approximation and Random Field Learning
Jun 1, 2026Mingtao Xia, Qijing ShenKolmogorov-Arnold Networks Neural Network Approximation Theory
University of Houston, Philip Guthrie Hoffman Hall, 3551 Cullen Blvd, Houston, 77204, Texas, United States of America · University of Birmingham, Watson Building, Birmingham, B15 2TT,, United Kingdom · University of Oxford, Henry Wellcome Building for Molecular Physiology, Old Road, Oxford, OX3 7BN, Oxfordshire, United Kingdom
LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
May 28, 2026Vincent GranvilleNeural Network Optimization Radial Basis Function Networks
CAIO
Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks
May 20, 2026Roberto Cavoretto, Alessandra De Rossi, Adeeba Haider +1Kolmogorov-Arnold Networks Kernel Methods
Department of Mathematics “Giuseppe Peano”, University of Torino, via Carlo Alberto 10, 10123 Torino, Italy · Member of the INdAM Research Group GNCS · Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China
Partition-of-Unity Gaussian Kolmogorov-Arnold Networks
Apr 26, 2026Amir NoorizadeganKolmogorov-Arnold Networks Kernel Methods
Department of Mathematics, Hong Kong Baptist University, Hong Kong SAR, China
Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions
Feb 5, 2026Yihao Ouyang, Shiwei Li, Haozhao Wang +5Low-Rank Adaptation Parameter-Efficient Fine-Tuning
Huazhong University of Science and Technology, Wuhan, China

Aug 3, 2026, cs.NEJ/K move · Enter open · S save

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models

Kei Nishihara, Yaochu Jin, Masaya Nakata

Organization for the Promotion of Education, Yokohama National University, Yokohama 2408501, Japan · School of Engineering, Westlake University, Hangzhou 310030, China · Faculty of Engineering, Yokohama National University, Yokohama 2408501, Japan

Surrogate Modeling Surrogate-Assisted Optimization Evolutionary Optimization Radial Basis Function Networks

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An ensemble of surrogate models helps improve the prediction quality and robustness of surrogate models, and in turn, the search performance of surrogate-assisted evolutionary algorithms (SAEAs). Although different degrees of smoothness of the approximated fitness landscapes need to be carefully designed for an effective ensemble, little attention has been paid to the explicit tuning of the degree of smoothness derived by surrogate models. This study proposes an adaptive ensemble SAEA, which automatically constructs plausible ensemble models by optimizing their parameter settings. Unlike existing adaptive/ensemble SAEAs, which consider prediction accuracy alone, the proposed algorithm optimizes the structure of radial basis function networks (RBFNs) by solving bi-objective minimization problems of approximation error and model complexity, resulting in robust ensemble models of accurate surrogate models with different degrees of smoothness of the approximated fitness landscapes. As a result, the over/under-fittings are reduced. Additionally, an infill criterion is designed so that surrogate models with different degrees of smoothness can contribute to the solution prescreening. The experimental results demonstrated the statistical superiority of our algorithm over state-of-the-art SAEAs on a single-objective benchmark and real-world problem sets under an expensive optimization scenario. The source code of the proposed algorithm is available at https://github.com/haranychan/EPOS

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