Results 31 to 40 of about 46,799 (193)

A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications

Journal of High Energy Physics, 2021
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate.
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld, Sascha Caron, Barry M. Dillon, Ben Farmer, Andrew Fowlie, Eduardo C. Garrido-Merchán, Will Handley, Luc Hendriks, Guðlaugur Jóhannesson, Adam Leinweber, Judita Mamužić, Gregory D. Martinez, Sydney Otten, Roberto Ruiz de Austri, Pat Scott, Zachary Searle, Bob Stienen, Joaquin Vanschoren, Martin White   +20 more
doaj   +1 more source

Biased Multiobjective Optimization and Decomposition Algorithm [PDF]

, 2016
The bias feature is a major factor that makes a multiobjective optimization problem (MOP) difficult for multiobjective evolutionary algorithms (MOEAs).
Deng, Jingda, Li, Hui, Zhang, Qingfu
core   +1 more source

Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm. [PDF]

, 2016
Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously.
Adeli, Antonelli, Bader, Blakelock, Bolourchi, Bringmann, Cerveira, Cheng, Cheng, Chow, Coello, Coello, Coello, Coletta, Corne, Deb, Deb, Deb, Emmerich, Etkin, Everson, Fleischer, Fonseca, Hansen, Helbig, Huband, Igel, Ishibuchi, Ishibuchi, Ishibuchi, Jain, Jaszkiewicz, Jia, Joly, Khare, Kim, Kociecki, Kociecki, Kociecki, Lara, Lee, Li, Lin, López-Jaimes, López-Jaimes, Menendez, Mesejo, Miettinen, Perez-Ramirez, Purshouse, Rashidi, Rostami, Sarma, Sarma, Shabbir, Siddique, Sindhya, Tabak, Tan, Tan, Van Moffaert, Vincenzi, Walker, Wang, While, Wilcoxon, Wolpert, Wu, Yuan, Zhang, Zhang, Zhang, Zhou, Zhu, Zitzler, Zou   +75 more
core   +2 more sources

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy

Sensors, 2023
In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning ...
Jianshuai Feng, Tianyu Shi, Yuankai Wu, Xiang Xie, Hongwen He, Huachun Tan   +5 more
doaj   +1 more source

On the Effect of Mirroring in the IPOP Active CMA-ES on the Noiseless BBOB Testbed [PDF]

, 2012
International audienceMirrored mutations and active covariance matrix adaptation are two recent ideas to improve the well-known covariance matrix adaptation evolution strategy (CMA-ES)---a state-of-the-art algorithm for numerical optimization.
Auger, Anne, Brockhoff, Dimo, Hansen, Nikolaus   +2 more
core   +3 more sources

Modified Covariance Matrix Adaptation – Evolution Strategy algorithm for constrained optimization under uncertainty, application to rocket design

International Journal for Simulation and Multidisciplinary Design Optimization, 2015
The design of complex systems often induces a constrained optimization problem under uncertainty. An adaptation of CMA-ES(λ, μ) optimization algorithm is proposed in order to efficiently handle the constraints in the presence of noise.
Chocat Rudy, Brevault Loïc, Balesdent Mathieu, Defoort Sébastien   +3 more
doaj   +1 more source

ICSPEA [PDF]

, 2007
The covariance matrix adaptation (CMA) is a concept originally introduced for improving the single-objective evolution strategy (ES). CMA varies the classical ES-mutation operator by utilising a mutation distribution adaptation scheme and an evolution ...
Kersting, Petra, Mehnen, Jörn, Rudolph, Günter, Tipura, Igor, Wagner, Tobias   +4 more
core   +1 more source

APPLICATION OF RESTART COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGY (RCMA-ES) TO GENERATION EXPANSION PLANNING PROBLEM [PDF]

ICTACT Journal on Soft Computing, 2012
This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMA-ES) to the Generation Expansion Planning (GEP) problem.
K. Karthikeyan, S. Kannan, S. Baskar, C. Thangaraj   +3 more
doaj  

Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing [PDF]

Journal of Advances in Computer Engineering and Technology, 2017
The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud ...
Ghazaal Emadi, Amir Masoud Rahmani, Hamed Shahhoseini   +2 more
doaj  

On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy

, 2019
Restart strategy helps the covariance matrix adaptation evolution strategy (CMA-ES) to increase the probability of finding the global optimum in optimization, while a single run CMA-ES is easy to be trapped in local optima.
Chen, Guanrong, Lou, Yang, Yuen, Shiu Yin, Zhang, Xin   +3 more
core   +1 more source