<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Doerr, Carola</style></author><author><style face="normal" font="default" size="100%">Bredeche, Nicolas</style></author><author><style face="normal" font="default" size="100%">Alba, Enrique</style></author><author><style face="normal" font="default" size="100%">Bartz-Beielstein, Thomas</style></author><author><style face="normal" font="default" size="100%">Brockhoff, Dimo</style></author><author><style face="normal" font="default" size="100%">Doerr, Benjamin</style></author><author><style face="normal" font="default" size="100%">Eiben, Gusz</style></author><author><style face="normal" font="default" size="100%">Epitropakis, Michael G.</style></author><author><style face="normal" font="default" size="100%">Fonseca, Carlos M.</style></author><author><style face="normal" font="default" size="100%">Guerreiro, Andreia</style></author><author><style face="normal" font="default" size="100%">Haasdijk, Evert</style></author><author><style face="normal" font="default" size="100%">Heinerman, Jacqueline</style></author><author><style face="normal" font="default" size="100%">Hubert, Julien</style></author><author><style face="normal" font="default" size="100%">Lehre, Per Kristian</style></author><author><style face="normal" font="default" size="100%">Malagò, Luigi</style></author><author><style face="normal" font="default" size="100%">Merelo, J. J.</style></author><author><style face="normal" font="default" size="100%">Miller, Julian</style></author><author><style face="normal" font="default" size="100%">Naujoks, Boris</style></author><author><style face="normal" font="default" size="100%">Oliveto, Pietro</style></author><author><style face="normal" font="default" size="100%">Picek, Stjepan</style></author><author><style face="normal" font="default" size="100%">Pillay, Nelishia</style></author><author><style face="normal" font="default" size="100%">Preuss, Mike</style></author><author><style face="normal" font="default" size="100%">Ryser-Welch, Patricia</style></author><author><style face="normal" font="default" size="100%">Squillero, Giovanni</style></author><author><style face="normal" font="default" size="100%">Stork, Jörg</style></author><author><style face="normal" font="default" size="100%">Sudholt, Dirk</style></author><author><style face="normal" font="default" size="100%">Tonda, Alberto</style></author><author><style face="normal" font="default" size="100%">Whitley, Darrell</style></author><author><style face="normal" font="default" size="100%">Zaefferer, Martin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Handl, Julia</style></author><author><style face="normal" font="default" size="100%">Hart, Emma</style></author><author><style face="normal" font="default" size="100%">Lewis, Peter R.</style></author><author><style face="normal" font="default" size="100%">López-Ibáñez, Manuel</style></author><author><style face="normal" font="default" size="100%">Ochoa, Gabriela</style></author><author><style face="normal" font="default" size="100%">Paechter, Ben</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tutorials at PPSN 2016</style></title><secondary-title><style face="normal" font="default" size="100%">Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-45823-6_95</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">1012–1022</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-45823-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</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%">D. K. Tasoulis</style></author><author><style face="normal" font="default" size="100%">N. G. Pavlidis</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><secondary-authors><author><style face="normal" font="default" size="100%">Ilias Maglogiannis</style></author><author><style face="normal" font="default" size="100%">Vassilis P. Plagianakos</style></author><author><style face="normal" font="default" size="100%">Ioannis Vlahavas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence: Theories and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">7297</style></volume><pages><style face="normal" font="default" size="100%">214-222</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-30447-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an “online” algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to reflect changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.</style></abstract></record><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%">D. K. Tasoulis</style></author><author><style face="normal" font="default" size="100%">N. G. Pavlidis</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%">Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Brisbane, Australia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.</style></abstract></record></records></xml>