Comparison of Monte Carlo and deterministic methods for non-adaptive optimization

被引：1
|
作者：
Al-Mharmah, HA [1 ]
Calvin, JM [1 ]
机构：
[1] Univ Jordan, Dept Ind Engn, Amman 11942, Jordan
来源：
PROCEEDINGS OF THE 1997 WINTER SIMULATION CONFERENCE | 1997年
关键词：
D O I：
10.1145/268437.268505
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.
引用
收藏
页码：348 / 351
页数：4
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