A Separability Prototype for Automatic Memes with Adaptive Operator Selection

TitleA Separability Prototype for Automatic Memes with Adaptive Operator Selection
Publication TypeConference Paper
Year of Publication2014
AuthorsEpitropakis, MG, Caraffini, F, Neri, F, Burke, EK
Conference NameFoundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on
Date PublishedDec
KeywordsAdaptation models, adaptive model, adaptive operator selection, Algorithm design and analysis, algorithmics, automatic design, Benchmark testing, hyper-heuristics, memetic computing, optimisation, optimization, optimization problems, Prototypes, search algorithms, search problems, search process, separability prototype for automatic memes, Software algorithms, software prototype, software prototyping, SPAM-AOS, Unsolicited electronic mail

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.

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