Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{andre:1996:aigp2,

• author = "David Andre and John R. Koza",
• title = "Parallel Genetic Programming: A Scalable Implementation Using The Transputer Network Architecture",
• booktitle = "Advances in Genetic Programming 2",
• publisher = "MIT Press",
• year = "1996",
• editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
• pages = "317--337",
• chapter = "16",
• address = "Cambridge, MA, USA",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-262-01158-1",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277532",
• URL = "https://dl.acm.org/citation.cfm?id=270224",
• DOI = "doi:10.7551/mitpress/1109.003.0022",
• size = "21 pages",
• abstract = "This chapter describes the parallel implementation of genetic programming in the C programming language using a PC type computer (running Windows) acting as a host and a network of processing nodes using the transputer architecture. 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. This approach is illustrated by a comparison 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 5% 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, the use of distributed sub-populations with only limited migration delivered more than linear speed-up in solving the problem.",

}

Genetic Programming entries for David Andre John Koza

Citations