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All Outputs (63)

Fairness in examination timetabling: student preferences and extended formulations (2017)
Journal Article
Muklason, A., Parkes, A. J., Özcan, E., McCollum, B., & McMullan, P. (2017). Fairness in examination timetabling: student preferences and extended formulations. Applied Soft Computing, 55, 302-318. https://doi.org/10.1016/j.asoc.2017.01.026

Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual... Read More about Fairness in examination timetabling: student preferences and extended formulations.

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation (2016)
Journal Article
Li, W., Özcan, E., & John, R. (2017). Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation. Renewable Energy, 105, https://doi.org/10.1016/j.renene.2016.12.022

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. Ho... Read More about Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation.

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem (2016)
Journal Article
Asta, S., Karapetyan, D., Kheiri, A., Özcan, E., & Parkes, A. J. (2016). Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem. Information Sciences, 373, 476-498. https://doi.org/10.1016/j.ins.2016.09.010

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of loc... Read More about Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem.

A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings (2016)
Journal Article
Özcan, E., Drake, J. H., Altıntaş, C., & Asta, S. (2016). A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings. Applied Soft Computing, 49, https://doi.org/10.1016/j.asoc.2016.07.032

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algori... Read More about A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings.

CHAMP: Creating Heuristics via Many Parameters for online bin packing (2016)
Journal Article
Asta, S., Özcan, E., & Parkes, A. J. (2016). CHAMP: Creating Heuristics via Many Parameters for online bin packing. Expert Systems with Applications, 63, 208-221. https://doi.org/10.1016/j.eswa.2016.07.005

The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. T... Read More about CHAMP: Creating Heuristics via Many Parameters for online bin packing.

A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem (2016)
Journal Article
Drake, J. H., Özcan, E., & Burke, E. (2016). A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem. Evolutionary Computation, 24(1), 113-141. https://doi.org/10.1162/EVCO_a_00145

© 2016 by the Massachusetts Institute of Technology. Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an exis... Read More about A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem.

A multi-agent based cooperative approach to scheduling and routing (2016)
Journal Article
Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169-178. https://doi.org/10.1016/j.ejor.2016.02.045

In this study, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperat... Read More about A multi-agent based cooperative approach to scheduling and routing.

Iterated local search using an add and delete hyper- heuristic for university course timetabling (2016)
Journal Article
Soria-Alcaraz, J. A., Özcan, E., Swan, J., Kendall, G., & Carpio, M. (2016). Iterated local search using an add and delete hyper- heuristic for university course timetabling. Applied Soft Computing, 40, https://doi.org/10.1016/j.asoc.2015.11.043

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete... Read More about Iterated local search using an add and delete hyper- heuristic for university course timetabling.

A tensor based hyper-heuristic for nurse rostering (2016)
Journal Article
Asta, S., Özcan, E., & Curtois, T. (2016). A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98, https://doi.org/10.1016/j.knosys.2016.01.031

Nurse rostering is a well-known highly constrained scheduling problem requiring assignment of shifts to nurses satisfying a variety of constraints. Exact algorithms may fail to produce high quality solutions, hence (meta)heuristics are commonly prefe... Read More about A tensor based hyper-heuristic for nurse rostering.

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.

A tensor-based selection hyper-heuristic for cross-domain heuristic search (2014)
Journal Article
Asta, S., & Özcan, E. (2015). A tensor-based selection hyper-heuristic for cross-domain heuristic search. Information Sciences, 299, https://doi.org/10.1016/j.ins.2014.12.020

Hyper-heuristics have emerged as automated high level search methodologies that manage a set of low level heuristics for solving computationally hard problems. A generic selection hyper-heuristic combines heuristic selection and move acceptance metho... Read More about A tensor-based selection hyper-heuristic for cross-domain heuristic search.

A constructive approach to examination timetabling based on adaptive decomposition and ordering (2014)
Journal Article
Abdul-Rahman, S., Burke, E., Bargiela, A., McCollum, B., & Özcan, E. (2014). A constructive approach to examination timetabling based on adaptive decomposition and ordering. Annals of Operations Research, 218(1), https://doi.org/10.1007/s10479-011-0999-8

In this study, we investigate an adaptive decomposition and ordering strategy that automatically divides examinations into difficult and easy sets for constructing an examination timetable. The examinations in the difficult set are considered to be h... Read More about A constructive approach to examination timetabling based on adaptive decomposition and ordering.

A stochastic local search algorithm with adaptive acceptance for high-school timetabling (2014)
Journal Article
Kheiri, A., Özcan, E., & Parkes, A. J. (2016). A stochastic local search algorithm with adaptive acceptance for high-school timetabling. Annals of Operations Research, 239(1), 135-151. https://doi.org/10.1007/s10479-014-1660-0

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per yea... Read More about A stochastic local search algorithm with adaptive acceptance for high-school timetabling.