Results 281 to 290 of about 2,841,488 (326)

An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES

arXiv.org
A robust and efficient optimization-based 2D/3D registration framework is crucial for the navigation system of orthopedic surgical robots. It can provide precise position information of surgical instruments and implants during surgery.
Minheng Chen, Tonglong Li, Zhirun Zhang, Youyong Kong   +3 more
semanticscholar   +1 more source

System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES

Journal of Marine Science and Technology, 2021
Accurate maneuvering estimation is essential to establish autonomous berthing control. The system-based mathematical model is widely used to estimate the ship’s maneuver.
Yoshiki Miyauchi, A. Maki, N. Umeda, D. M. Rachman, Youhei Akimoto   +4 more
semanticscholar   +1 more source

Sampling in CMA-ES: Low Numbers of Low Discrepancy Points

International Joint Conference on Computational Intelligence
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is one of the most successful examples of a derandomized evolution strategy. However, it still relies on randomly sampling offspring, which can be done via a uniform distribution and ...
J. D. Nobel, D. Vermetten, Thomas H. W. Back, Anna V. Kononova   +3 more
semanticscholar   +1 more source

CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points

Parallel Problem Solving from Nature
Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integer and continuous variables, and several integer handlings have been ...
Kento Uchida, Ryoki Hamano, Masahiro Nomura, Shota Saito, Shinichi Shirakawa   +4 more
semanticscholar   +1 more source

CMA-ES for one-class constraint synthesis

Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020
We propose CMA-ES for One-Class Constraint Synthesis (CMAESOCCS), a method that synthesizes Mixed-Integer Linear Programming (MILP) model from exemplary feasible solutions to this model using Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES).
Marcin Karmelita, Tomasz P. Pawlak
openaire   +1 more source

Parameters Optimization Method for ADRC of Permanent Magnet Synchronous Motor Based on CMA-ES

Cybersecurity and Cyberforensics Conference
Active disturbance rejection control (ADRC) is widely used to control permanent magnet synchronous motors (PMSM). However, the settings of gain parameters in ADRC is often dependent on engineers’ experience. Also, it is difficult to design the parameters
Tian-yuan Jiang, Yang Yang, Zhiyuan Li, Tianqi Yang, Xuwen Chen   +4 more
semanticscholar   +1 more source

A Combination of CMA-ES with Probability Distributions of Integer Variables for Mixed-Integer Black-Box Optimization

GECCO Companion
In this paper, we propose a method for efficiently solving the mixed-integer black-box optimization problem by utilizing the probability distribution models of integer variables in the CMA-ES algorithm. Firstly, some elite points among the generated ones
Duc Manh Nguyen
semanticscholar   +1 more source

Optimization of NOX emissions of a CRDI DIESEL engine using CMA-ES method

International Journal of Engine Research
Engine calibration is the tuning of embedded parameters in the engine control unit (ECU) software to improve vehicle characteristics and meet legal requirements.
Seyfullah Berk, Ertan Alptekin
semanticscholar   +1 more source

Towards an Adaptive CMA-ES Configurator

2018
Recent work has shown that significant performance gains over state-of-the-art CMA-ES variants can be obtained by a recombination of their algorithmic modules. It seems plausible that further improvements can be realized by an adaptive selection of these configurations.
van Rijn, Sander, Doerr, Carola, Back, Thomas   +2 more
openaire   +2 more sources

Uncertainty handling CMA-ES for reinforcement learning

Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an adaptive uncertainty handling mechanism. Because uncertainty is a typical property of RL problems this new algorithm, termed UH-CMA-ES, is promising for RL.
Verena Heidrich-Meisner, Christian Igel
openaire   +1 more source