Skip to search formSkip to main contentSkip to account menu

Stochastic optimization

Known as: Stochastic optimisation, Stochastic search 
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables… 
Wikipedia (opens in a new tab)

Related topics

Related topics

48 relations
AIMMSCMA-ESConstructive cooperative coevolutionCross-entropy method

Broader (1)

Estimation theory

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2013
Highly Cited
2013
Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface
We present an evolutionary stochastic search algorithm to obtain a discrete representation of the protein energy surface in terms… 
Highly Cited
2011
Highly Cited
2011
Learning attentional policies for tracking and recognition in video with deep networks
We propose a novel attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by… 
Highly Cited
2008
Highly Cited
2008
Particle swarm approaches using Lozi map chaotic sequences to fuzzy modelling of an experimental thermal-vacuum system
2008
2008
Shadow prices of environmental outputs and production efficiency of household-level paper recycling units in Vietnam
Highly Cited
2003
Highly Cited
2003
On the Pricing of Forward Starting Options under Stochastic Volatility
We consider the problem of pricing European forward starting options in the presence of stochastic ­volatility. By performing a… 
Highly Cited
1996
Highly Cited
1996
Inspection allocation in manufacturing systems using stochastic search techniques
Quality is the hallmark of a competitive product. It is necessary to use inspection stations to check product quality and process… 
Review
1996
Review
1996
A Comprehensive Review of Production-oriented Manufacturing Cell Formation Techniques
This paper o ers a comprehensive review and classi cation of techniques to manipulate part routing sequences for manufacturing… 
Highly Cited
1994
Highly Cited
1994
A hybrid algorithm for finding the global minimum of error function of neural networks and its applications
1987
1987
Methods for Selecting the Optimal Dynamic Hedge When Production is Stochastic
A dynamic hedging problem with stochastic production is solved. The optimal feedback rules recognize that future hedges will be… 
Highly Cited
1981
Highly Cited
1981
APPROXIMATION AND CONVERGENCE IN NONLINEAR OPTIMIZATION
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)