Parallel Genetic Programming on a Network of Transputers
Parallel Genetic Programming on a Network of Transputers
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发表时间：
1995
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通讯作者：
J. Koza;D. Andre
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作者：
J. Koza;D. Andre
该报告描述了遗传程序设计的并行实现在C编程语言使用PC 486型计算机（运行Windows）作为主机和网络的晶片机作为处理节点。使用这种方法，遗传算法和遗传编程的研究人员可以以介于两者之间的成本获得介于当前可用工作站的能力和超级计算机的能力之间的计算能力。一个比较的计算工作所需的解决问题的布尔偶5奇偶函数的符号回归不同的迁移率。遗传编程需要最少的计算量，迁移率为8%。此外，这种计算工作量小于用串行计算机和相同大小的随机种群解决问题所需的工作量。也就是说，除了在遗传编程的并行实现中固有的执行固定量的代码的接近线性加速之外，并行化在使用遗传编程解决问题时提供了比线性加速更多的加速。
This report describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer (running Windows) acting as a host and a network of transputers acting as processing nodes. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. A comparison is made of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with an 8% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, parallelization delivered more than linear speed-up in solving the problem using genetic programming.