<?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></records></xml>