Using a Queue Genetic Algorithm to Evolve Xpilot Control Strategies on a Distributed System

Author

Parker, Matt ; Parker, Gary B.

Author_Institution

Indiana Univ., Bloomington

fYear

0

fDate

0-0 0

Firstpage

1202

Lastpage

1207

Abstract

In this paper, we describe a distributed learning system used to evolve a control program for an agent operating in the network game Xpilot. This system, which we refer to as a queue genetic algorithm, is a steady state genetic algorithm that uses stochastic selection and first-in-first-out replacement. We employ it to distribute fitness evaluations over a local network of dissimilar computers. The system made full use of our available computers while evolving successful controller solutions that were comparable to those evolved using a regular generational genetic algorithm.

Keywords

computer games; distributed control; genetic algorithms; learning systems; queueing theory; Xpilot control strategy; agent operation; distributed learning system; first-in-first-out replacement; network game Xpilot; queue genetic algorithm; steady state genetic algorithm; stochastic selection; Computer networks; Control systems; Distributed computing; Genetic algorithms; Humans; Internet; Learning systems; Neural networks; Steady-state; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9487-9

Type

conf

DOI

10.1109/CEC.2006.1688446

Filename

1688446