DocumentCode
2688555
Title
Calibrating strategies for evolutionary algorithms
Author
Montero, Elizabeth ; Riff, María-Cristina
Author_Institution
Univ. Tecnica Federico Santa Maria, Valparaiso
fYear
2007
fDate
25-28 Sept. 2007
Firstpage
394
Lastpage
399
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CEC.2007.4424498
Filename
4424498
Link To Document