Fault-tolerant network computation of individuals in genetic algorithms

Author

Hamilton-Wright, Andrew ; Stacey, Deborah

Author_Institution

Waterloo Univ., Ont., Canada

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1721

Lastpage

1726

Abstract

Many genetic algorithms have complex fitness functions which can easily be calculated in parallel, given the tools to do so. This paper explores the use of a tool to gather spare computing cycles from a variable set of machines to allow convergence of GAs of this type. A modification to the steady-state model for GAs allows us to use the fault-prone behavior of an underlying thin networked computation system as noise within the GA itself. This "real" noise is incorporated into the GA, maintaining the drive towards convergence in the case of the heavily noisy network environment

Keywords

backpropagation; fault tolerant computing; genetic algorithms; backpropagation; complex fitness functions; computing cycles; fault-prone behavior; fault-tolerant network computation; genetic algorithms; steady-state model; Computer networks; Design engineering; Fault tolerance; Genetic algorithms; Genetic engineering; Information science; Intelligent networks; Steady-state; Systems engineering and theory; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004502

Filename

1004502