Calibrating strategies for evolutionary algorithms

Elizabeth Montero, Maria-Cristina Riff
2007 2007 IEEE Congress on Evolutionary Computation  
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
more » ... and HAEA a random parameter control.
doi:10.1109/cec.2007.4424498 dblp:conf/cec/MonteroR07 fatcat:aq3yhxdpg5ccjmkepnfxjk77sm