A massively parallel GP engine in VLSI

In this paper we propose the implementation of a massively parallel genetic programming (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.

[1]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[2]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[3]  Morgan Kaufmann,et al.  A Massively Distributed Parallel Genetic Algorithm (mdpGA , 1992 .

[4]  K. Haase,et al.  Experiences with Fine-Grained Parallel Genetic Algorithms , 1996 .

[5]  Peter Nordin,et al.  AIM-GP and parallelism , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[7]  Markus Schwehm,et al.  Parallel Population Models for Genetic Algorithms , 1996 .

[8]  Erick Cantú-Paz.  Designing efficient master-slave parallel genetic algorithms , 1997 .

[9]  Wolfgang Banzhaf,et al.  Genetic Programming: An Introduction , 1997 .

[10]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[11]  David Abramson,et al.  A PARALLEL GENETIC ALGORITHM FOR SOLVING THE SCHOOL TIMETABLING PROBLEM , 1992 .

[12]  John R. Koza,et al.  Building a Parallel Computer System for $18, 000 that Performs a Half Peta-Flop per Day , 1999, GECCO.

[13]  Hartmut Schmeck,et al.  Experiences with fine‐grainedparallel genetic algorithms , 1999, Ann. Oper. Res..

[14]  A. D. Bethke,et al.  Comparison of genetic algorithms and gradient-based optimizers on parallel processors : efficiency of use of processing capacity , 1976 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .