Title :
Reinforcement learning in steady-state cellular genetic algorithms
Author :
Lee, Cin-Young ; Antonsson, Erik K.
fDate :
6/24/1905 12:00:00 AM
Abstract :
A novel cellular genetic algorithm is developed to address the issues of good mate selection. This is accomplished through reinforcement learning where good mating individuals attract and poor mating individuals repel. Adaptation of good mate choice occurs, thus leading to more efficient search. Results are presented for various test cases
Keywords :
genetic algorithms; learning (artificial intelligence); good mate selection; reinforcement learning; search; steady-state cellular genetic algorithms; Computation theory; Computational modeling; Convergence; Genetic algorithms; Learning; Production; Robustness; Steady-state; Testing; Topology;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1004514
