TY - CONF T1 - A Separability Prototype for Automatic Memes with Adaptive Operator Selection T2 - Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on Y1 - 2014 A1 - Epitropakis, M.G. A1 - Caraffini, F. A1 - Neri, F. A1 - Burke, E.K. KW - Adaptation models KW - adaptive model KW - adaptive operator selection KW - Algorithm design and analysis KW - algorithmics KW - automatic design KW - Benchmark testing KW - hyper-heuristics KW - memetic computing KW - optimisation KW - optimization KW - optimization problems KW - Prototypes KW - search algorithms KW - search problems KW - search process KW - separability prototype for automatic memes KW - Software algorithms KW - software prototype KW - software prototyping KW - SPAM-AOS KW - Unsolicited electronic mail AB - 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. JF - Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on ER -