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

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 replacem...

Full description

Saved in:
Bibliographic Details
Published in2006 IEEE International Conference on Evolutionary Computation pp. 1202 - 1207
Main Authors Parker, M., Parker, G.B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Computer networks
Control systems
Distributed computing
Genetic algorithms
Humans
Internet
Learning systems
Neural networks
Steady-state
Stochastic systems
Online AccessGet full text
ISBN9780780394872
0780394879
ISSN1089-778X
DOI10.1109/CEC.2006.1688446

Cover

More Information
Summary: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.
ISBN:9780780394872
0780394879
ISSN:1089-778X
DOI:10.1109/CEC.2006.1688446