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An iterated multi-stage selection hyper-heuristic (2015)
Journal Article
Kheiri, A., & Özcan, E. (2016). An iterated multi-stage selection hyper-heuristic. European Journal of Operational Research, 250(1), https://doi.org/10.1016/j.ejor.2015.09.003

There is a growing interest towards the design of reusable general purpose search methods that are applicable to di?erent problems instead of tailored solutions to a single particular problem. Hyper-heuristics have emerged as such high level methods... Read More about An iterated multi-stage selection hyper-heuristic.

Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey (2015)
Journal Article
Deveci, M., Çetin Demirel, N., John, R., & Özcan, E. (in press). Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey. Journal of Natural Gas Science and Engineering, 27(2), https://doi.org/10.1016/j.jngse.2015.09.004

The problem of choosing the best location for CO2 storage is a crucial and challenging multi-criteria decision problem for some companies. This study compares the performance of three fuzzy-based multi-criteria decision making (MCDM) methods, includi... Read More about Fuzzy multi-criteria decision making for carbon dioxide geological storage in Turkey.

Solving high school timetabling problems worldwide using selection hyper-heuristics (2015)
Journal Article
Ahmed, L. N., Özcan, E., & Kheiri, A. (2015). Solving high school timetabling problems worldwide using selection hyper-heuristics. Expert Systems with Applications, 42(13), https://doi.org/10.1016/j.eswa.2015.02.059

High school timetabling is one of those recurring NP-hard real-world combinatorial optimisation problems that has to be dealt with by many educational institutions periodically, and so has been of interest to practitioners and researchers. Solving a... Read More about Solving high school timetabling problems worldwide using selection hyper-heuristics.

A grouping hyper-heuristic framework: application on graph colouring (2015)
Journal Article
Elhag, A., & Özcan, E. (2015). A grouping hyper-heuristic framework: application on graph colouring. Expert Systems with Applications, 42(13), https://doi.org/10.1016/j.eswa.2015.01.038

Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purp... Read More about A grouping hyper-heuristic framework: application on graph colouring.

A software interface for supporting the application of data science to optimisation (2015)
Journal Article
Parkes, A. J., Özcan, E., & Karapetyan, D. (2015). A software interface for supporting the application of data science to optimisation. Lecture Notes in Artificial Intelligence, 8994, 306-311. https://doi.org/10.1007/978-3-319-19084-6_31

Many real world problems can be solved effectively by metaheuristics in combination with neighbourhood search. However, implementing neighbourhood search for a particular problem domain can be time consuming and so it is important to get the most val... Read More about A software interface for supporting the application of data science to optimisation.

Detecting change and dealing with uncertainty in imperfect evolutionary environments (2015)
Journal Article
Mujtaba, H., Kendall, G., Baig, A. R., & Özcan, E. (2015). Detecting change and dealing with uncertainty in imperfect evolutionary environments. Information Sciences, 302, https://doi.org/10.1016/j.ins.2014.12.053

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environme... Read More about Detecting change and dealing with uncertainty in imperfect evolutionary environments.

Choice function based hyper-heuristics for multi-objective optimization (2015)
Journal Article
Özcan, E. (2015). Choice function based hyper-heuristics for multi-objective optimization. Applied Soft Computing, 28, https://doi.org/10.1016/j.asoc.2014.12.012

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution... Read More about Choice function based hyper-heuristics for multi-objective optimization.