<?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%">S. K. Tasoulis</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%">D. K. Tasoulis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Density Based Projection Pursuit Clustering</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%">Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data.</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><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</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%">Enhancing Differential Evolution Utilizing Proximity-based Mutation Operators</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Evolutionary Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">99-119</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.</style></abstract></record></records></xml>