<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. G. Epitropakis</style></author><author><style face="normal" font="default" size="100%">V. P. Plagianakos</style></author><author><style face="normal" font="default" size="100%">M. N. Vrahatis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolutionary Adaptation of the Differential Evolution Control Parameters</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2009. CEC 2009</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">adaptive control</style></keyword><keyword><style  face="normal" font="default" size="100%">differential evolution control parameter</style></keyword><keyword><style  face="normal" font="default" size="100%">evolutionary adaptation</style></keyword><keyword><style  face="normal" font="default" size="100%">evolutionary computation</style></keyword><keyword><style  face="normal" font="default" size="100%">optimisation</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">self-adaptive differential evolution algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">self-adjusting systems</style></keyword><keyword><style  face="normal" font="default" size="100%">user-defined parameter tuning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Trondheim, Norway</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes a novel self-adaptive scheme for the evolution of crucial control parameters in evolutionary algorithms. More specifically, we suggest to utilize the differential evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is responsible for the evolution of the crucial user-defined mutation and recombination constants. This self-adaptive differential evolution algorithm alleviates the need of tuning these user-defined parameters while maintains the convergence properties of the original algorithm. The evolutionary self-adaptive scheme is evaluated through several well-known optimization benchmark functions and the experimental results indicate that the proposed approach is promising.</style></abstract></record></records></xml>