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WSC
1998
13 years 5 months ago
Stopping Criterion for a Simulation-Based Optimization Method
We consider a new simulation-based optimization method called the Nested Partitions (NP) method. This method generates a Markov chain and solving the optimization problem is equiv...
Sigurdur Ólafsson, Leyuan Shi
SIAMJO
2002
124views more  SIAMJO 2002»
13 years 4 months ago
The Sample Average Approximation Method for Stochastic Discrete Optimization
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
GECCO
2007
Springer
135views Optimization» more  GECCO 2007»
13 years 10 months ago
A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms
In this work we present a novel and efficient algorithm– independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorit...
Luis Martí, Jesús García, Ant...
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
13 years 11 months ago
A stopping criterion based on Kalman estimation techniques with several progress indicators
The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteri...
José Luis Guerrero, Jesús Garc&iacut...
WSC
2004
13 years 6 months ago
Simulation-Based Optimization Using Simulated Annealing With Confidence Interval
This paper develops a variant of Simulated Annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be eva...
Talal M. Alkhamis, Mohamed A. Ahmed