Calibrating strategies for evolutionary algorithms

被引：3
|
作者：
Montero, Elizabeth [1 ]
Riff, Maria-Cristina [1 ]
机构：
[1] Univ Tecn Federico Santa Maria, Dept Comp Sci, Valparaiso, Chile
来源：
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/CEC.2007.4424498
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.
引用
收藏
页码：394 / 399
页数：6
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