A scalable cellular implementation of parallel genetic programming

被引：47
作者：
Folino, G [1 ]
Pizzuti, C [1 ]
Spezzano, G [1 ]
机构：
[1] Univ Calabria, ICAR, CNR, DEIS, I-87036 Arcavacata Di Rende, CE, Italy
来源：
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2003年 / 7卷 / 01期
关键词：
cellular genetic programming model; genetic programming (GP); load balance; parallel processing; scalability;
D O I：
10.1109/TEVC.2002.806168
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The method adopts a load-balancing policy that avoids the unequal utilization of the processors. Experimental results on benchmark problems of different complexity show the superiority of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.
引用
收藏
页码：37 / 53
页数：17
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