Results 41 to 50 of about 46,799 (193)
, 2016 We discuss the current minimisation strategies adopted by research projects involving the determination of parton distribution functions (PDFs) and fragmentation functions (FFs) through the training of neural networks.
N. Hartland, S. Carrazza
core +3 more sources
Constrained optimization of a zoom lens with CMA-ES algorithm [PDF]
EPJ Web of ConferencesIn the present paper we investigate how optimization algorithm can be tailored to improve the lens design process. We replaced gradient-based optimization methods by the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).
Marty Tristan +2 more
doaj +1 more source
Deriving and improving CMA-ES with Information geometric trust regions [PDF]
, 2017 CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning.
Abdolmaleki, Abbas +4 more
core +1 more source
Applied Mechanics, 2022 The presented research work demonstrates an efficient methodology based on a micromechanical framework for the prediction of the effective elastic properties of strongly bonded long-fiber-reinforced materials (CFRP) used for the construction of tubular ...
Ioannis Zyganitidis +2 more
doaj +1 more source
CMA-ES with Two-Point Step-Size Adaptation [PDF]
, 2008 We combine a refined version of two-point step-size adaptation with the covariance matrix adaptation evolution strategy (CMA-ES). Additionally, we suggest polished formulae for the learning rate of the covariance matrix and the recombination weights.
Hansen, Nikolaus
core +3 more sources
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES [PDF]
, 2014 The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems.
C. Igel +4 more
core +5 more sources
Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy
Evolutionary ComputationAbstract Evolution-based neural architecture search methods have shown promising results, but they require high computational resources because these methods involve training each candidate architecture from scratch and then evaluating its fitness, which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Sinha, Nilotpal, Chen, Kuan-Wen
openaire +3 more sources
An adaptive ES with a ranking based constraint handling strategy [PDF]
Yugoslav Journal of Operations Research, 2014 To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited.
Kusakci Ali Osman, Can Mehmet
doaj +1 more source
A Bayesian Damage Identification Technique Using Evolutionary Algorithms - a Comparative Study
Electronic Journal of Structural Engineering, 2015 In this paper, a one-stage model-based damage identification technique based on the response power spectral density of a structure is investigated.
M. Varmazyar, Nick Haritos, M. Kirley
doaj +1 more source
An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning
Connection Science, 2020 Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed.
Fuqing Zhao +5 more
doaj +1 more source