A Massively Parallel GP Engine in VLSI

author = "Sven E. Eklund",

title = "A Massively Parallel {GP} Engine in {VLSI}",

booktitle = "Proceedings of the 2002 Congress on Evolutionary Computation CEC2002",

editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and Mark Shackleton",

pages = "629--633",

year = "2002",

publisher = "IEEE Press",

publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",

organisation = "IEEE Neural Network Council (NNC), Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",

ISBN = "0-7803-7278-6",

month = "12-17 " # may,

notes = "CEC 2002 - A joint meeting of the IEEE, the Evolutionary Programming Society, and the IEE. Held in connection with the World Congress on Computational Intelligence (WCCI 2002)",

keywords = "genetic algorithms, genetic programming, VHDL simulations, VLSI, diffusion model, linear machine code, massively parallel architecture, search space, VLSI, mathematics computing, parallel architectures",

DOI = "

doi:10.1109/CEC.2002.1006999",

abstract = "In this paper we propose the implementation of a massively parallel GP model in hardware in order to speed up the genetic algorithm. This fine-grained diffusion architecture consists of a large amount of independent processing nodes that evolve a large number of small, overlapping subpopulations. Every node has an embedded CPU that executes a linear machine code GP representation at a rate of up to 20,000 generations per second.",