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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 different 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.

Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing (2015)
Presentation / Conference Contribution
Tyasnurita, R., Özcan, E., Shahriar, A., & John, R. Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing. Presented at 15th UK Workshop on Computational Intelligence (UKCI 2015)

A hyper-heuristic is a heuristic optimisation method which generates or selects heuristics (move operators) based on a set of components while solving a computationally difficult problem. Apprenticeship learning arises while observing the behavior of... Read More about Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing.

Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks (2015)
Presentation / Conference Contribution
Araujo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., & Hague, R. J. Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks. Presented at Annual International Solid Freeform Fabrication Symposium

In many cases, the efficient operation of Additive Manufacturing (AM) technology relies on build volumes being packed effectively. Packing algorithms have been developed in response to this requirement. The configuration of AM build volumes is partic... Read More about Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks.

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 tensor analysis improved genetic algorithm for online bin packing (2015)
Presentation / Conference Contribution
Asta, S., & Özcan, E. A tensor analysis improved genetic algorithm for online bin packing. Presented at Genetic and Evolutionary Computation Conference (2015)

Mutation in a Genetic Algorithm is the key variation operator adjusting the genetic diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value of a gene. In this study, we describe... Read More about A tensor analysis improved genetic algorithm for online bin packing.

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.

A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex (2015)
Presentation / Conference Contribution
Drake, J. H., Özcan, E., & Burke, E. K. A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex. Presented at 2015 IEEE Congress on Evolutionary Computation (CEC2015)

Hyper-heuristics are search methodologies which operate at a higher level of abstraction than traditional search and optimisation techniques. Rather than operating on a search space of solutions directly, a hyper-heuristic searches a space of low-lev... Read More about A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex.

A modified choice function hyper-heuristic controlling unary and binary operators (2015)
Presentation / Conference Contribution
Drake, J. H., Özcan, E., & Burke, E. K. A modified choice function hyper-heuristic controlling unary and binary operators. Presented at 2015 IEEE Congress on Evolutionary Computation (CEC2015)

Hyper-heuristics are a class of high-level search methodologies which operate on a search space of low-level heuristics or components, rather than on solutions directly. Traditional iterative selection hyper-heuristics rely on two key components, a h... Read More about A modified choice function hyper-heuristic controlling unary and binary operators.

A simulated annealing approach to supplier selection aware inventory planning (2015)
Presentation / Conference Contribution
Turk, S., Miller, S., Özcan, E., & John, R. A simulated annealing approach to supplier selection aware inventory planning. Presented at 2015 IEEE Congress on Evolutionary Computation (CEC2015)

Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. Also, appropriate inventory management is critical to the success of a supply chain operation. In recent years, there has been a gro... Read More about A simulated annealing approach to supplier selection aware inventory planning.

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.

Heuristic generation via parameter tuning for online bin packing (2015)
Presentation / Conference Contribution
Yarimcam, A., Asta, S., Ozcan, E., & Parkes, A. J. (2014, December). Heuristic generation via parameter tuning for online bin packing. Presented at IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings, Orlando, FL, USA

© 2014 IEEE. Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long st... Read More about Heuristic generation via parameter tuning for online bin packing.

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.

An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex (2014)
Presentation / Conference Contribution
Asta, S., & Özcan, E. An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex. Presented at 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

Apprenticeship learning occurs via observations while an expert is in action. A hyper-heuristic is a search method or a learning mechanism that controls a set of low level heuristics or combines different heuristic components to generate heuristics f... Read More about An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex.