Theory of coevolutionary genetic algorithms

被引：0
|
作者：
Schmitt, LM [1 ]
机构：
[1] Univ Aizu, Aizu Wakamatsu, Fukushima 9658580, Japan
来源：
PARALLEL AND DISTRIBUTED PROCESSING AND APPLICATIONS, PROCEEDINGS | 2003年 / 2745卷
关键词：
coevolution; convergence of genetic algorithms; simulated annealing; parallel processing;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
We discuss stochastic modeling of scaled coevolutionary genetic algorithms (coevGA) which converge asymptotically to global optima. In our setting, populations contain several types of interacting creatures such that for some types (appropriately defined) globally maximal creatures exist. These algorithms particularly demand parallel processing in view of the nature of the fitness function. It is shown that coevolutionary arms races yielding global optima can be implemented in a procedure similar to simulated annealing.
引用
收藏
页码：285 / 293
页数：9
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