Results 71 to 80 of about 46,799 (193)

Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization

Applied Sciences
Multimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problems ...
Huan Liu, Lijing Qin, Zhao Zhou
doaj   +1 more source

The CMA Evolution Strategy: A Tutorial

, 2016
This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try
Hansen, Nikolaus
core  

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization [PDF]

, 2013
This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization.
Loshchilov, Ilya, Schoenauer, Marc, Sebag, Michèle   +2 more
core   +3 more sources

Robust Covariance Adaptation in Adaptive Importance Sampling

, 2018
Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution.
Bugallo, Monica F., El-Laham, Yousef, Elvira, Victor   +2 more
core   +1 more source

A Collaborative Auto-Diversified Optimization Scheme  [PDF]

Journal of Universal Computer Science
We present a Collaborative Auto-Diversified Optimization Scheme (CADOS) for solving continuous and combinatorial optimization problems. CADOS aims to explore the synergy of various optimization algorithms and enhance their effectiveness and efficiency ...
Besma Hezili, Hichem Talbi
doaj   +3 more sources

A covariance matrix adaptation evolution strategy in reproducing kernel Hilbert space

Genetic Programming and Evolvable Machines, 2019
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well-defined parameter space in which feature functions are often defined manually. Therefore, the performance of those techniques strongly depends on the quality of the chosen features ...
Viet-Hung Dang, Ngo Anh Vien, T. J. Chung   +2 more
openaire   +3 more sources

On the Effectiveness of Nature-Inspired Metaheuristic Algorithms for Performing Phase Equilibrium Thermodynamic Calculations

The Scientific World Journal, 2014
The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research.
Seif-Eddeen K. Fateen, Adrian Bonilla-Petriciolet   +1 more
doaj   +1 more source

A Covariance Matrix Adaptation Evolution Strategy for Direct Policy Search in Reproducing Kernel Hilbert Space [PDF]

, 2017
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free optimization algorithm. It optimizes a black-box objective function over a well defined parameter space.
Chung, TaeChoong, Dang, Viet-Hung, Vien, Ngo Anh   +2 more
core  

Using Gaussian processes as surrogate models for the CMA evolution strategy [PDF]

, 2016
Tato práce zkoumá efektivitu metod založených na Gaussovských procesech v oblasti spojité black-box optimalizace. Tyto metody slouží jako náhradní modely pro CMA evoluční strategii. Práce popisuje několik nejmodernějších metod a pak srovnává jejích výkon
Orekhov Nikita
core  

Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms

Applied Sciences
This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of
Shurong Peng, Zexiang Guo, Xiaoxu Liu, Tan Zhang, Yunhao Yang   +4 more
doaj   +1 more source