Title :
A genetic algorithm-based controller for decentralized multi-agent robotic systems
Author :
Agah, Arvin ; Bekey, George A.
Author_Institution :
Bio-Robotics Div., AIST-MITI, Tsukuba, Japan
Abstract :
In this paper the results of evolution on the task performance of a robot colony are discussed. The cognitive architecture of individual robots of a colony are modified, using genetic algorithms, producing a generation of robots with superior task performance, compared with those of the initial robot population. The effects of mutation probability and fitness scaling parameters on simulated evolution are also studied in this paper
Keywords :
cooperative systems; decentralised control; genetic algorithms; intelligent control; optimal control; probability; robots; software agents; cognitive architecture; decentralized multi-agent robotic systems; evolution; fitness scaling parameters; genetic algorithm-based controller; mutation probability; robot colony; simulated evolution; task performance; Cognitive robotics; Control systems; Genetic algorithms; Genetic mutations; Laboratories; Mechanical engineering; Mobile robots; Robot sensing systems; Robotics and automation; Wheels;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542403
