@inbook {39,
	title = {Tutorials at PPSN 2016},
	booktitle = {Parallel Problem Solving from Nature {\textendash} PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
	year = {2016},
	pages = {1012{\textendash}1022},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!},
	isbn = {978-3-319-45823-6},
	doi = {10.1007/978-3-319-45823-6_95},
	url = {http://dx.doi.org/10.1007/978-3-319-45823-6_95},
	author = {Doerr, Carola and Bredeche, Nicolas and Alba, Enrique and Bartz-Beielstein, Thomas and Brockhoff, Dimo and Doerr, Benjamin and Eiben, Gusz and Epitropakis, Michael G. and Fonseca, Carlos M. and Guerreiro, Andreia and Haasdijk, Evert and Heinerman, Jacqueline and Hubert, Julien and Lehre, Per Kristian and Malag{\`o}, Luigi and Merelo, J. J. and Miller, Julian and Naujoks, Boris and Oliveto, Pietro and Picek, Stjepan and Pillay, Nelishia and Preuss, Mike and Ryser-Welch, Patricia and Squillero, Giovanni and Stork, J{\"o}rg and Sudholt, Dirk and Tonda, Alberto and Whitley, Darrell and Zaefferer, Martin},
	editor = {Handl, Julia and Hart, Emma and Lewis, Peter R. and L{\'o}pez-Ib{\'a}{\~n}ez, Manuel and Ochoa, Gabriela and Paechter, Ben}
}
@inbook {41,
	title = {Repairing and Optimizing Hadoop hashCode Implementations},
	booktitle = {Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings},
	year = {2014},
	pages = {259{\textendash}264},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {We describe how contract violations in Java TM hashCode methods can be repaired using novel combination of semantics-preserving and generative methods, the latter being achieved via Automatic Improvement Programming. The method described is universally applicable. When applied to the Hadoop platform, it was established that it produces hashCode functions that are at least as good as the original, broken method as well as those produced by a widely-used alternative method from the {\textquoteleft}Apache Commons{\textquoteright} library.},
	isbn = {978-3-319-09940-8},
	doi = {10.1007/978-3-319-09940-8_22},
	url = {http://dx.doi.org/10.1007/978-3-319-09940-8_22},
	author = {Kocsis, Zoltan A. and Neumann, Geoff and Swan, Jerry and Epitropakis, Michael G. and Brownlee, Alexander E. I. and Haraldsson, Sami O. and Bowles, Edward},
	editor = {Le Goues, Claire and Yoo, Shin}
}
@conference {34,
	title = {A Separability Prototype for Automatic Memes with Adaptive Operator Selection},
	booktitle = {Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on},
	year = {2014},
	month = {Dec},
	abstract = {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.},
	keywords = {Adaptation 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},
	doi = {10.1109/FOCI.2014.7007809},
	author = {Epitropakis, M.G. and Caraffini, F. and Neri, F. and Burke, E.K.}
}
