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AMC
2008
88views more  AMC 2008»
13 years 10 months ago
Stopping rules for box-constrained stochastic global optimization
We present three new stopping rules for Multistart based methods. The first uses a device that enables the determination of the coverage of the bounded search domain. The second i...
Isaac E. Lagaris, Ioannis G. Tsoulos
JGO
2010
89views more  JGO 2010»
13 years 8 months ago
Stopping rules in k-adaptive global random search algorithms
In this paper we develop a methodology for defining stopping rules in a general class of global random search algorithms that are based on the use of statistical procedures. To bu...
Anatoly A. Zhigljavsky, Emily Hamilton
SIAMJO
2002
124views more  SIAMJO 2002»
13 years 9 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...
CDC
2008
IEEE
130views Control Systems» more  CDC 2008»
13 years 10 months ago
Optimal stopping for event-triggered sensing and actuation
Novel event-triggered sensing and actuation strategies are presented for networked control systems with limited communication resources. Two architectures are considered: one with ...
Maben Rabi, Karl Henrik Johansson, Mikael Johansso...
GECCO
2007
Springer
135views Optimization» more  GECCO 2007»
14 years 4 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...