<?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%">Epitropakis, M.G.</style></author><author><style face="normal" font="default" size="100%">Caraffini, F.</style></author><author><style face="normal" font="default" size="100%">Neri, F.</style></author><author><style face="normal" font="default" size="100%">Burke, E.K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Separability Prototype for Automatic Memes with Adaptive Operator Selection</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation models</style></keyword><keyword><style  face="normal" font="default" size="100%">adaptive model</style></keyword><keyword><style  face="normal" font="default" size="100%">adaptive operator selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithm design and analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">algorithmics</style></keyword><keyword><style  face="normal" font="default" size="100%">automatic design</style></keyword><keyword><style  face="normal" font="default" size="100%">Benchmark testing</style></keyword><keyword><style  face="normal" font="default" size="100%">hyper-heuristics</style></keyword><keyword><style  face="normal" font="default" size="100%">memetic computing</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%">optimization problems</style></keyword><keyword><style  face="normal" font="default" size="100%">Prototypes</style></keyword><keyword><style  face="normal" font="default" size="100%">search algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">search problems</style></keyword><keyword><style  face="normal" font="default" size="100%">search process</style></keyword><keyword><style  face="normal" font="default" size="100%">separability prototype for automatic memes</style></keyword><keyword><style  face="normal" font="default" size="100%">Software algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">software prototype</style></keyword><keyword><style  face="normal" font="default" size="100%">software prototyping</style></keyword><keyword><style  face="normal" font="default" size="100%">SPAM-AOS</style></keyword><keyword><style  face="normal" font="default" size="100%">Unsolicited electronic mail</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the main challenges in algorithmics in general, and in Memetic Computing, in particular, is the automatic design of search algorithms. A recent advance in this direction (in terms of continuous problems) is the development of a software prototype that builds up an algorithm based upon a problem analysis of its separability. This prototype has been called the Separability Prototype for Automatic Memes (SPAM). This article modifies the SPAM by incorporating within it an adaptive model used in hyper-heuristics for tackling optimization problems. This model, namely Adaptive Operator Selection (AOS), rewards at run time the most promising heuristics/memes so that they are more likely to be used in the following stages of the search process. The resulting framework, here referred to as SPAM-AOS, has been tested on various benchmark problems and compared with modern algorithms representing the-state-of-the-art of search for continuous problems. Numerical results show that the proposed SPAM-AOS is a promising framework that outperforms the original SPAM and other modern algorithms. Most importantly, this study shows how certain areas of Memetic Computing and Hyper-heuristics are very closely related topics and it also shows that their combination can lead to the development of powerful algorithmic frameworks.</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%">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>