Title :
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
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;
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
DOI :
10.1109/CEC.2006.1688446
