<?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%">Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2010. CEC 2010. (IEEE World Congress on Computational Intelligence)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cognitive experience</style></keyword><keyword><style  face="normal" font="default" size="100%">convergence</style></keyword><keyword><style  face="normal" font="default" size="100%">differential evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">evolutionary computation</style></keyword><keyword><style  face="normal" font="default" size="100%">particle swarm optimisation</style></keyword><keyword><style  face="normal" font="default" size="100%">particle swarm optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">social experience</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Barcelona, Spain</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm near to the vicinity of problem’s optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience of the swarm with the Differential Evolution algorithm. Extensive experimental results on twelve high dimensional multimodal benchmark functions indicate that the hybrid variants are very promising and improve the original algorithm.</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%">N. G. Pavlidis</style></author><author><style face="normal" font="default" size="100%">E. G. Pavlidis</style></author><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%">Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Congress on Evolutionary Computation, 2007. CEC 2007</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational intelligence algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">differential evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">financial market</style></keyword><keyword><style  face="normal" font="default" size="100%">foreign exchange market</style></keyword><keyword><style  face="normal" font="default" size="100%">foreign exchange trading</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized moving average rule</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern detection</style></keyword><keyword><style  face="normal" font="default" size="100%">risk analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">risk-adjusted trading strategy</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical testing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Singapore</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper investigates the performance of trading strategies identified through computational intelligence techniques. We focus on trading rules derived by genetic programming, as well as, generalized moving average rules optimized through differential evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but genetic programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.</style></abstract></record></records></xml>