Title :
Calibrating strategies for evolutionary algorithms
Author :
Montero, Elizabeth ; Riff, María-Cristina
Author_Institution :
Univ. Tecnica Federico Santa Maria, Valparaiso
Abstract :
The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HaEa a random parameter control.
Keywords :
evolutionary computation; learning (artificial intelligence); adaptive techniques; calibrating strategies; evolutionary algorithms; open research area; parameter control strategies; random parameter control; reinforcement learning; tuning techniques; Adaptive control; Algorithm design and analysis; Costs; Entropy; Evolutionary computation; Genetic algorithms; Iterative algorithms; Learning; Programmable control; Testing;
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
DOI :
10.1109/CEC.2007.4424498
