Skip to main content

Research Repository

Advanced Search

Outputs (97)

A benchmark dataset for multi-objective flexible job shop cell scheduling (2023)
Journal Article
Deliktaş, D., Özcan, E., Ustun, O., & Torkul, O. (2024). A benchmark dataset for multi-objective flexible job shop cell scheduling. Data in Brief, 52, Article 109946. https://doi.org/10.1016/j.dib.2023.109946

This data article presents a description of a benchmark dataset for the multi-objective flexible job shop scheduling problem in a cellular manufacturing environment. This problem considers intercellular moves, exceptional parts, sequence-dependent fa... Read More about A benchmark dataset for multi-objective flexible job shop cell scheduling.

Ensemble strategy using particle swarm optimisation variant and enhanced local search capability (2023)
Journal Article
Hong, L., Wang, G., Özcan, E., & Woodward, J. (2023). Ensemble strategy using particle swarm optimisation variant and enhanced local search capability. Swarm and Evolutionary Computation, 84, Article 101452. https://doi.org/10.1016/j.swevo.2023.101452

Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing... Read More about Ensemble strategy using particle swarm optimisation variant and enhanced local search capability.

A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization (2023)
Journal Article
Hong, L., Yu, X., Tao, G., Özcan, E., & Woodward, J. (2024). A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization. Complex and Intelligent Systems, 10(2), 2421-2443. https://doi.org/10.1007/s40747-023-01269-z

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-param... Read More about A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization.

An adaptive greedy heuristic for large scale airline crew pairing problems (2023)
Journal Article
Zeren, B., Özcan, E., & Deveci, M. (2024). An adaptive greedy heuristic for large scale airline crew pairing problems. Journal of Air Transport Management, 114, Article 102492. https://doi.org/10.1016/j.jairtraman.2023.102492

A crew pairing represents a sequence of flight legs that constitute a crew work allocation, starting and ending at the same crew base. A complete set of crew pairings covers all flight legs in the timetable of an airline for a given planning horizon.... Read More about An adaptive greedy heuristic for large scale airline crew pairing problems.

ML meets MLn: machine learning in ligand promoted homogeneous catalysis (2023)
Journal Article
Hirst, J. D., Boobier, S., Coughlan, J., Streets, J., Jacob, P. L., Pugh, O., …Woodward, S. (2023). ML meets MLn: machine learning in ligand promoted homogeneous catalysis. Artificial Intelligence Chemistry, 1(2), Article 100006. https://doi.org/10.1016/j.aichem.2023.100006

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as condu... Read More about ML meets MLn: machine learning in ligand promoted homogeneous catalysis.

Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies (2023)
Journal Article
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Jaouen, L., & Bécot, F. (2023). Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies. Journal of the Acoustical Society of America, 153(5), Article 2945. https://doi.org/10.1121/10.0019455

When designing passive sound-attenuation structures, one of the challenging problems that arise is optimally distributing acoustic porous materials within a design region so as to maximise sound absorption while minimising material usage. To identify... Read More about Multi-objective topology optimisation for acoustic porous materials using gradient-based, gradient-free, and hybrid strategies.

Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation (2023)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., Özcan, E., & Ninić, J. (2023). Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation. Composites Part B: Engineering, 258, Article 110714. https://doi.org/10.1016/j.compositesb.2023.110714

Automatic Fibre Placement manufacturing processes have become the aerospace industry standard for the production of large-scale composite components. Besides the challenges linked with the manufacturing of such components, their design process is als... Read More about Layup time for an Automated Fibre Placement process in the framework of a detailed sizing optimisation.

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem (2023)
Journal Article
Sonuç, E., & Özcan, E. (2023). An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem. Expert Systems with Applications, 224, Article 119956. https://doi.org/10.1016/j.eswa.2023.119956

Metaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for thei... Read More about An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem.

An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase (2023)
Journal Article
Hong, L., Yu, X., Wang, B., Woodward, J., & Özcan, E. (2023). An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase. Swarm and Evolutionary Computation, 78, Article 101278. https://doi.org/10.1016/j.swevo.2023.101278

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed,... Read More about An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase.

Stacking sequence optimisation of an aircraft wing skin (2023)
Journal Article
Ntourmas, G., Glock, F., Deinert, S., Daoud, F., Schuhmacher, G., Chronopoulos, D., …Ninić, J. (2023). Stacking sequence optimisation of an aircraft wing skin. Structural and Multidisciplinary Optimization, 66(2), Article 31. https://doi.org/10.1007/s00158-022-03483-8

This paper demonstrates a stacking sequence optimisation process of a composite aircraft wing skin. A two-stage approach is employed to satisfy all sizing requirements of this industrial sized, medium altitude, long endurance drone. In the first stag... Read More about Stacking sequence optimisation of an aircraft wing skin.

An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms (2022)
Journal Article
Gümüş, D. B., Özcan, E., Atkin, J., & Drake, J. H. (2023). An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms. Information Sciences, 619, 153-171. https://doi.org/10.1016/j.ins.2022.11.008

Parameter tuning is a challenging and time-consuming task, crucial to obtaining improved metaheuristic performance. There is growing interest in cross-domain search methods, which consider a range of optimisation problems rather than being specialise... Read More about An investigation of F-Race training strategies for cross domain optimisation with memetic algorithms.

Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles (2022)
Journal Article
Corrado, G., Ntourmas, G., Sferza, M., Traiforos, N., Arteiro, A., Brown, L., …Turner, T. (2022). Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles. Progress in Aerospace Sciences, 135, Article 100861. https://doi.org/10.1016/j.paerosci.2022.100861

Designing an airframe is a complex process as it requires knowledge from multiple disciplines such as aerodynamics, structural mechanics, manufacturing, flight dynamics, which individually lead to very different optimal designs. Furthermore, the grow... Read More about Recent progress, challenges and outlook for multidisciplinary structural optimization of aircraft and aerial vehicles.

Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations (2022)
Journal Article
Hey, J., Siebers, P. O., Nathanail, P., Ozcan, E., & Robinson, D. (2022). Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations. Journal of Building Performance Simulation, https://doi.org/10.1080/19401493.2022.2106309

Modelling energy retrofit adoption in domestic urban building stocks is vital for policymakers aiming to reduce emissions. The use of surrogate models to evaluate building performance combined with optimization procedures can optimize small building... Read More about Surrogate optimization of energy retrofits in domestic building stocks using household carbon valuations.

Many-objective test case generation for graphical user interface applications via search-based and model-based testing (2022)
Journal Article
de Santiago, V. A., Özcan, E., & Balera, J. M. (2022). Many-objective test case generation for graphical user interface applications via search-based and model-based testing. Expert Systems with Applications, 208, Article 118075. https://doi.org/10.1016/j.eswa.2022.118075

The majority of the studies that generate test cases for graphical user interface (GUI) applications are based on or address functional requirements only. In spite of the fact that interesting approaches have been proposed, they do not address functi... Read More about Many-objective test case generation for graphical user interface applications via search-based and model-based testing.

A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse (2022)
Journal Article
Deveci, M., Mishra, A. R., Gokasar, I., Rani, P., Pamucar, D., & Ozcan, E. (2023). A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse. IEEE Transactions on Fuzzy Systems, 31(2), 475-484. https://doi.org/10.1109/TFUZZ.2022.3190613

Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This study introduces a hybrid dec... Read More about A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse.

Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task (2022)
Journal Article
Kok, Y. E., Woodward, S., Özcan, E., & Torres Torres, M. (2022). Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task. Molecular Informatics, 41(12), Article 2200068. https://doi.org/10.1002/minf.202200068

Chirality, the ability of some molecules to exist as two non-superimposable mirror images, profoundly influences both chemistry and biology. Advances in deep learning enable the automatic recognition of chemical structure diagrams, however, studies o... Read More about Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection (2021)
Journal Article
Deveci, M., Öner, S. C., Ciftci, M. E., Özcan, E., & Pamucar, D. (2022). Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection. Applied Soft Computing, 114, Article 108076. https://doi.org/10.1016/j.asoc.2021.108076

Choosing the most appropriate aircraft type for a given route is one of the crucial issues that the decision makers at airline companies have to address under uncertainty based on various commercial, marketing and operational criteria. A novel multi-... Read More about Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection.

Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems (2021)
Journal Article
de Carvalho, V. R., Özcan, E., & Sichman, J. S. (2021). Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems. Applied Sciences, 11(19), Article 9153. https://doi.org/10.3390/app11199153

As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-triv... Read More about Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems.

Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials (2021)
Journal Article
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Sreekumar, A., Jaouen, L., & Bécot, F. (2021). Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials. Journal of the Acoustical Society of America, 150(4), 3164-3175. https://doi.org/10.1121/10.0006784

When designing sound packages, often fully filling the available space with acoustic materials is not the most absorbing solution. Better solutions can be obtained by creating cavities of air pockets, but determining the most optimal shape and topolo... Read More about Comparison of heuristics and metaheuristics for topology optimisation in acoustic porous materials.

Evolutionary algorithms for multi-objective flexible job shop cell scheduling (2021)
Journal Article
Deliktaş, D., Özcan, E., Ustun, O., & Torkul, O. (2021). Evolutionary algorithms for multi-objective flexible job shop cell scheduling. Applied Soft Computing, 113(Part A), Article 107890. https://doi.org/10.1016/j.asoc.2021.107890

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transpor... Read More about Evolutionary algorithms for multi-objective flexible job shop cell scheduling.

Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming (2021)
Journal Article
Hong, L., Woodward, J. R., Özcan, E., & Liu, F. (2021). Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming. Complex and Intelligent Systems, 7(6), 3135-3163. https://doi.org/10.1007/s40747-021-00507-6

Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study... Read More about Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming.

Generic stacks and application of composite rules for the detailed sizing of laminated structures (2021)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., & Özcan, E. (2021). Generic stacks and application of composite rules for the detailed sizing of laminated structures. Composite Structures, 276, Article 114487. https://doi.org/10.1016/j.compstruct.2021.114487

Two-stage approaches are commonly applied to optimise the stacking sequence of large-scale composite structures. The two stages consist of a gradient and non-gradient based optimisation addressing the mixed nature of continuous and discrete constrain... Read More about Generic stacks and application of composite rules for the detailed sizing of laminated structures.

Metaheuristics “In the Large” (2021)
Journal Article
Swan, J., Adriaensen, S., Johnson, C. G., Kheiri, A., Krawiec, F., Merelo, J. J., …White, D. R. (2022). Metaheuristics “In the Large”. European Journal of Operational Research, 297(2), 393-406. https://doi.org/10.1016/j.ejor.2021.05.042

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need... Read More about Metaheuristics “In the Large”.

Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS (2021)
Journal Article
Deveci, M., Özcan, E., John, R., Pamucar, D., & Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, Article 107532. https://doi.org/10.1016/j.asoc.2021.107532

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electr... Read More about Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS.

L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout (2021)
Journal Article
Song, H., Torres Torres, M., Özcan, E., & Triguero, I. (2021). L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout. Neurocomputing, 442, 200-208. https://doi.org/10.1016/j.neucom.2021.02.024

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural network... Read More about L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout.

Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures (2021)
Journal Article
Ntourmas, G., Glock, F., Daoud, F., Schuhmacher, G., Chronopoulos, D., & Özcan, E. (2021). Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures. Composite Structures, 264, 113660. https://doi.org/10.1016/j.compstruct.2021.113660

This manuscript proposes two novel formulations for the manufacturable stacking sequence retrieval of laminated composite structures. Detailed sizing of composite structures is commonly tackled by a two-stage optimisation approach, the first stage be... Read More about Mixed Integer Linear Programming formulations of the stacking sequence and blending optimisation of composite structures.

Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance (2020)
Journal Article
Santiago Júnior, V. A. D., Özcan, E., & Carvalho, V. R. D. (2020). Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance. Applied Soft Computing, 97(Part A), Article 106760. https://doi.org/10.1016/j.asoc.2020.106760

In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement Learn... Read More about Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance.

Acoustic topology optimisation using CMA-ES (2020)
Conference Proceeding
Ramamoorthy, V. T., Ozcan, E., Parkes, A., Sreekumar, A., Jaouen, L., & Becot, F. (2020). Acoustic topology optimisation using CMA-ES. In Proceedings of ISMA2020 International Conference on Noise and Vibration Engineering: USD2020 International Conference on Uncertainty in Structural Dynamics

Structural topology optimisation techniques are increasingly being applied to acoustic materials. Most acoustic topology optimisation applications use the solid-isotropic-material-with-penalization (SIMP) approach [1]–[4] which is a derivative-based... Read More about Acoustic topology optimisation using CMA-ES.

Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations (2020)
Journal Article
Türk, S., Deveci, M., Özcan, E., Canıtez, F., & John, R. (2021). Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations. Information Sciences, 547, 641-666. https://doi.org/10.1016/j.ins.2020.08.076

Electric vehicles are the key to facilitating the transition to low-carbon ‘green’ transport. However, there are concerns with their range and the location of the charging stations which delay a full-fledged adoption of their use. Hence, the electric... Read More about Interval type-2 fuzzy sets improved by Simulated Annealing for locating the electric charging stations.

Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem (2020)
Conference Proceeding
Garza-Santisteban, F., Amaya, I., Cruz-Duarte, J., Ortiz-Bayliss, J. C., Ozcan, E., & Terashima-Marin, H. (2020). Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem. In 2020 IEEE Congress on Evolutionary Computation (CEC) (1-8). https://doi.org/10.1109/CEC48606.2020.9185709

This study presents an offline learning Simulated Annealing approach to generate a constructive hyper-heuristic evaluated through training and testing on a set of instances for solving the Job-Shop Scheduling problem. The generated hyperheuristic use... Read More about Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem.

Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge (2020)
Journal Article
Kheiri, A., Ahmed, L., Boyacı, B., Gromicho, J., Mumford, C., Özcan, E., & Dirikoç, A. S. (2020). Exact and hyper‐heuristic solutions for the distribution‐installation problem from the VeRoLog 2019 challenge. Networks, 76(2), 294-319. https://doi.org/10.1002/net.21962

This work tackles a rich vehicle routing problem (VRP) problem integrating a capacitated vehicle routing problem with time windows (CVRPTW), and a service technician routing and scheduling problem (STRSP) for delivering various equipment based on cus... Read More about Exact and hyper?heuristic solutions for the distribution?installation problem from the VeRoLog 2019 challenge.

A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method (2020)
Journal Article
Deveci, M., Özcan, E., John, R., Covrig, C., & Pamucar, D. (2020). A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method. Journal of Environmental Management, 270, Article 110916. https://doi.org/10.1016/j.jenvman.2020.110916

This study investigates the degree of importance of criteria affecting the optimal site selection of offshore wind farms. Firstly, forty two different influential criteria have been selected by reviewing the scientific literature on offshore wind far... Read More about A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method.

A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation (2020)
Journal Article
Farshi, T. R., Drake, J. H., & Özcan, E. (2020). A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation. Expert Systems with Applications, 149, Article 113233. https://doi.org/10.1016/j.eswa.2020.113233

Color image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three s... Read More about A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation.

Metaheuristic optimisation of sound absorption performance of multilayered porous materials (2019)
Conference Proceeding
Ramamoorthy, V. T., Özcan, E., Parkes, A. J., Luc, J., & Bécot, F. (2019). Metaheuristic optimisation of sound absorption performance of multilayered porous materials. In Proceedings of the ICA 2019 and EAA Euroregio 23rd International Congress on Acoustics, integrating 4th EAA Euroregio 2019 9 - 13 September 2019, Aachen, Germany (3213-3220)

The optimization of multilayered-sound-packaging is a challenging task which involves searching the best/op-timal settings for a number of acoustic parameters. The search space size becomes too large to handle by brute force, as the number of those p... Read More about Metaheuristic optimisation of sound absorption performance of multilayered porous materials.

Recent advances in selection hyper-heuristics (2019)
Journal Article
Drake, J. H., Kheiri, A., Özcan, E., & Burke, E. K. (2020). Recent advances in selection hyper-heuristics. European Journal of Operational Research, 285(2), 405-428. https://doi.org/10.1016/j.ejor.2019.07.073

Hyper-heuristics have emerged as a way to raise the level of generality of search techniques for computational search problems. This is in contrast to many approaches, which represent customised methods for a single problem domain or a narrow class o... Read More about Recent advances in selection hyper-heuristics.

Towards a streamlined stacking sequence optimisation methodology for blended composite aircraft structures (2019)
Conference Proceeding
Ntourmas, G., Özcan, E., Chronopoulos, D., Glock, F., & Daoud, F. (2019). Towards a streamlined stacking sequence optimisation methodology for blended composite aircraft structures. In Proceedings of 8th European Conference for Aeronautics and Aerospace Sciences (EUCASS)

In order to fully exploit the benefits provided by using composite materials in large scale aerospace structures, more efficient detailed design optimisation techniques need to be developed. In the present work, the optimisation procedure is split up... Read More about Towards a streamlined stacking sequence optimisation methodology for blended composite aircraft structures.

Fuzzy Hot Spot Identification for Big Data: An Initial Approach (2019)
Conference Proceeding
Triguero, I., Tickle, R., Figueredo, G. P., Mesgarpour, M., Ozcan, E., & John, R. I. (2019). Fuzzy Hot Spot Identification for Big Data: An Initial Approach. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858979

Hot spot identification problems are present across a wide range of areas, such as transportation, health care and energy. Hot spots are locations where a certain type of event occurs with high frequency. A recent big data approach is capable of iden... Read More about Fuzzy Hot Spot Identification for Big Data: An Initial Approach.

A study on the interpretability of a fuzzy system to control an inverted pendulum (2019)
Conference Proceeding
Zeren, B., Deveci, M., Coupland, S., John, R., & Ender¨ozcan, E. (2019). A study on the interpretability of a fuzzy system to control an inverted pendulum.

Fuzzy systems mimic human reasoning and provide solutions to problems under uncertainty via 'computing with words'. This particular strength of fuzzy systems is often discarded in some real world applications where the fuzzy sets are designed for con... Read More about A study on the interpretability of a fuzzy system to control an inverted pendulum.

A review on the self and dual interactions between machine learning and optimisation (2019)
Journal Article
Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8(2), 143–165. https://doi.org/10.1007/s13748-019-00185-z

Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from... Read More about A review on the self and dual interactions between machine learning and optimisation.

Learning the Quality of Dispatch Heuristics Generated by Automated Programming (2018)
Book Chapter
Parkes, A. J., Beglou, N., & Ozcan, E. (2019). Learning the Quality of Dispatch Heuristics Generated by Automated Programming. In Learning and Intelligent Optimization (154-158). Springer Verlag. https://doi.org/10.1007/978-3-030-05348-2_13

One of the challenges within the area of optimisation, and AI in general, is to be able to support the automated creation of the heuristics that are often needed within effective algorithms. Such an example of automated programming may be performed b... Read More about Learning the Quality of Dispatch Heuristics Generated by Automated Programming.

Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset (2018)
Journal Article
Araújo, L. J., Özcan, E., Atkin, J. A., & Baumers, M. (2019). Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset. International Journal of Production Research, 57(18), 5920-5934. https://doi.org/10.1080/00207543.2018.1534016

© 2018 Informa UK Limited, trading as Taylor & Francis Group. With most Additive Manufacturing (AM) technology variants, build processes take place inside an internal enclosed build container, referred to as a ‘build volume’. It has been demonstrat... Read More about Analysis of irregular three-dimensional packing problems in additive manufacturing: a new taxonomy and dataset.

A classification of hyper-heuristic approaches: Revisited (2018)
Book Chapter
Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2019). A classification of hyper-heuristic approaches: Revisited. In Handbook of metaheuristics (453-477). Cham: Springer Publishing Company. https://doi.org/10.1007/978-3-319-91086-4_14

© Springer International Publishing AG, part of Springer Nature 2019. Hyper-heuristics comprise a set of approaches that aim to automate the development of computational search methodologies. This chapter overviews previous categorisations of hyper-h... Read More about A classification of hyper-heuristic approaches: Revisited.

Move acceptance in local search metaheuristics for cross-domain search (2018)
Journal Article
Jackson, W. G., Özcan, E., & John, R. I. (2018). Move acceptance in local search metaheuristics for cross-domain search. Expert Systems with Applications, 131, https://doi.org/10.1016/j.eswa.2018.05.006

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and have been successfully applied to a range of computationally hard real-world problems. Local search metaheuristics operate under a single-point based s... Read More about Move acceptance in local search metaheuristics for cross-domain search.

Evolutionary computation for wind farm layout optimization (2018)
Journal Article
Wilson, D., Rodrigues, S., Segura, C., Loshchilov, I., Huttor, F., Buenfil, G. L., …Sylvain, C. (2018). Evolutionary computation for wind farm layout optimization. Renewable Energy, 126, https://doi.org/10.1016/j.renene.2018.03.052

This paper presents the results of the second edition of the Wind Farm Layout Optimization Competition, which was held at the 22nd Genetic and Evolutionary Computation COnference (GECCO) in 2015. During this competition, competitors were tasked with... Read More about Evolutionary computation for wind farm layout optimization.

Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey (2018)
Journal Article
Deveci, M., Özcan, E., John, R., & Öner, S. C. (2018). Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey. Journal of Air Transport Management, 69, https://doi.org/10.1016/j.jairtraman.2018.01.008

This study investigates the level of service quality of domestic airlines in Turkey travelling between Istanbul and London and compares those airline companies according to a set of predetermined criteria. A practical multi-criteria decision making a... Read More about Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey.

Proposal of a design pattern for embedding the concept of social forces in human centric simulation models (2018)
Conference Proceeding
Siebers, P., Deng, Y., Thaler, J., Schnädelbach, H., & Özcan, E. (2018). Proposal of a design pattern for embedding the concept of social forces in human centric simulation models. In A. Anagnostou, M. Fakhimi, R. Meskarian, & D. Robertson (Eds.), Proceedings of the Operational Research Society Simulation Workshop 2018 (SW18) (88-97)

There exist many papers that explain the social force model and its application for modelling pedestrian dynamics. None of these papers, however, explains how to implement the social force model in order to use it for systems simulation studies. In t... Read More about Proposal of a design pattern for embedding the concept of social forces in human centric simulation models.

To kit or not to kit: analysing the value of model-based kitting for additive manufacturing (2018)
Journal Article
Khajavi, S. H., Baumers, M., Holmström, J., Özcan, E., Atkin, J., Jackson, W. G., & Li, W. (2018). To kit or not to kit: analysing the value of model-based kitting for additive manufacturing. Computers in Industry, 98, https://doi.org/10.1016/j.compind.2018.01.022

The use of additive manufacturing (AM) for the production of functional parts is increasing. Thus, AM based practices that can reduce supply chain costs gain in importance. We take a forward-looking approach and study how AM can be used more effectiv... Read More about To kit or not to kit: analysing the value of model-based kitting for additive manufacturing.

A Learning Automata-Based Multiobjective Hyper-Heuristic (2017)
Journal Article
Li, W., Özcan, E., & John, R. (2019). A Learning Automata-Based Multiobjective Hyper-Heuristic. IEEE Transactions on Evolutionary Computation, 23(1), 59-73. https://doi.org/10.1109/TEVC.2017.2785346

© 1997-2012 IEEE. Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight... Read More about A Learning Automata-Based Multiobjective Hyper-Heuristic.

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming (2017)
Journal Article
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (in press). A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Applied Soft Computing, 62, https://doi.org/10.1016/j.asoc.2017.10.002

Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy,... Read More about A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming.

Automated generation of constructive ordering heuristics for educational timetabling (2017)
Journal Article
Pillay, N., & Özcan, E. (2017). Automated generation of constructive ordering heuristics for educational timetabling. Annals of Operations Research, 275, 181-208. https://doi.org/10.1007/s10479-017-2625-x

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For... Read More about Automated generation of constructive ordering heuristics for educational timetabling.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Conference Proceeding
Tyasnurita, R., Özcan, E., & John, R. (2017). Learning heuristic selection using a time delay neural network for open vehicle routing.

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

Sparse, continuous policy representations for uniform online bin packing via regression of interpolants (2017)
Journal Article
Swan, J., Drake, J. H., Neumann, G., & Özcan, E. (2017). Sparse, continuous policy representations for uniform online bin packing via regression of interpolants. Lecture Notes in Artificial Intelligence, 10197, 189-200. https://doi.org/10.1007/978-3-319-55453-2_13

Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation o... Read More about Sparse, continuous policy representations for uniform online bin packing via regression of interpolants.

Multi-objective optimisation in inventory planning with supplier selection (2017)
Journal Article
Turk, S., Özcan, E., & John, R. (2017). Multi-objective optimisation in inventory planning with supplier selection. Expert Systems with Applications, 78, https://doi.org/10.1016/j.eswa.2017.02.014

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we pre... Read More about Multi-objective optimisation in inventory planning with supplier selection.

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.

An investigation of tuning a memetic algorithm for cross-domain search (2016)
Conference Proceeding
Gumus, D. B., Özcan, E., & Atkin, J. (2016). An investigation of tuning a memetic algorithm for cross-domain search. In 2016 IEEE Congress on Evolutionary Computation (CEC): 24-29 July 2016 Vancouver, Canada (135-142). https://doi.org/10.1109/CEC.2016.7743788

Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial s... Read More about An investigation of tuning a memetic algorithm for cross-domain search.

Performance of selection hyper-heuristics on the extended HyFlex domains (2016)
Conference Proceeding
Almutairi, A., Özcan, E., Kheiri, A., & Jackson, W. G. (2016). Performance of selection hyper-heuristics on the extended HyFlex domains. In Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings (154-162). https://doi.org/10.1007/978-3-319-47217-1_17

Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected... Read More about Performance of selection hyper-heuristics on the extended HyFlex domains.

An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget (2016)
Conference Proceeding
Gümüş, D. B., Özcan, E., & Atkin, J. (2016). An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget. In Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings (12–20). https://doi.org/10.1007/978-3-319-47217-1_2

Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budge... Read More about An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget.

Ensemble move acceptance in selection hyper-heuristics (2016)
Conference Proceeding
Kheiri, A., Mısır, M., & Özcan, E. (2016). Ensemble move acceptance in selection hyper-heuristics. In Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings (21–29). https://doi.org/10.1007/978-3-319-47217-1_3

Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new... Read More about Ensemble move acceptance in selection hyper-heuristics.

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.

A comparative study of fuzzy parameter control in a general purpose local search metaheuristic (2016)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. I. (2016). A comparative study of fuzzy parameter control in a general purpose local search metaheuristic. . https://doi.org/10.1109/CEC.2016.7743787

There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of... Read More about A comparative study of fuzzy parameter control in a general purpose local search metaheuristic.

Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic (2016)
Conference Proceeding
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (2016). Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference - GECCO '16 (725-732). https://doi.org/10.1145/2908812.2908958

In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the fun... Read More about Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic.

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.

Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing (2015)
Conference Proceeding
Tyasnurita, R., Özcan, E., Shahriar, A., & John, R. (2015). Improving performance of a hyper-heuristic using a multilayer perceptron for vehicle routing.

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)
Conference Proceeding
Araujo, L., Özcan, E., Atkin, J., Baumers, M., Tuck, C., & Hague, R. J. (2015). Toward better build volume packing in additive manufacturing: classification of existing problems and benchmarks.

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)
Conference Proceeding
Asta, S., & Özcan, E. (2015). A tensor analysis improved genetic algorithm for online bin packing. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15. https://doi.org/10.1145/2739480.2754787

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)
Conference Proceeding
Drake, J. H., Özcan, E., & Burke, E. K. (2015). A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex. In 2015 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2015.7257316

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)
Conference Proceeding
Drake, J. H., Özcan, E., & Burke, E. K. (2015). A modified choice function hyper-heuristic controlling unary and binary operators. In 2015 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2015.7257315

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)
Conference Proceeding
Turk, S., Miller, S., Özcan, E., & John, R. (2015). A simulated annealing approach to supplier selection aware inventory planning. In 2015 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2015.7257105

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)
Conference Proceeding
Yarimcam, A., Asta, S., Ozcan, E., & Parkes, A. J. (2015). Heuristic generation via parameter tuning for online bin packing. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS) (102-108). https://doi.org/10.1109/EALS.2014.7009510

© 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)
Conference Proceeding
Asta, S., & Özcan, E. (2014). An apprenticeship learning hyper-heuristic for vehicle routing in HyFlex. In 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS). https://doi.org/10.1109/EALS.2014.7009505

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.

Modified choice function heuristic selection for the Multidimensional Knapsack Problem (2014)
Conference Proceeding
Drake, J. H., Özcan, E., & Burke, E. K. (2014). Modified choice function heuristic selection for the Multidimensional Knapsack Problem. In Genetic and evolutionary computing (225–234). https://doi.org/10.1007/978-3-319-12286-1_23

Hyper-heuristics are a class of high-level search methods used to solve computationally difficult problems, which operate on a search space of low-level heuristics rather than solutions directly. Previous work has shown that selection hyper-heuristic... Read More about Modified choice function heuristic selection for the Multidimensional Knapsack Problem.

Interval type-2 fuzzy sets in supplier selection (2014)
Conference Proceeding
Turk, S., John, R., & Özcan, E. (2014). Interval type-2 fuzzy sets in supplier selection. In 2014 14th UK Workshop on Computational Intelligence (UKCI). https://doi.org/10.1109/UKCI.2014.6930168

Selection of an appropriate supplier is a crucial and challenging task in the effective management of a supply chain. This study introduces a model for solving the supplier selection problem using interval type-2 fuzzy sets. Moreover, the influence o... Read More about Interval type-2 fuzzy sets in supplier selection.

Fuzzy adaptive parameter control of a late acceptance hyper-heuristic (2014)
Conference Proceeding
Jackson, W. G., Özcan, E., & John, R. I. (2014). Fuzzy adaptive parameter control of a late acceptance hyper-heuristic. In 2014 14th UK Workshop on Computational Intelligence (UKCI)

A traditional iterative selection hyper-heuristic which manages a set of low level heuristics relies on two core components, a method for selecting a heuristic to apply at a given point, and a method to decide whether or not to accept the result of t... Read More about Fuzzy adaptive parameter control of a late acceptance hyper-heuristic.

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.

A genetic programming hyper-heuristic for the multidimensional knapsack problem (2014)
Journal Article
Drake, J. H., Hyde, M., Khaled, I., & Özcan, E. (2014). A genetic programming hyper-heuristic for the multidimensional knapsack problem. Kybernetes, 43(9/10), https://doi.org/10.1108/K-09-2013-0201

Purpose: Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic prog... Read More about A genetic programming hyper-heuristic for the multidimensional knapsack problem.

Constructing constrained-version of magic squares using selection hyper-heuristics (2014)
Journal Article
Kheiri, A., & Özcan, E. (2014). Constructing constrained-version of magic squares using selection hyper-heuristics. Computer Journal, 57(3), https://doi.org/10.1093/comjnl/bxt130

A square matrix of distinct numbers in which every row, column and both diagonals have the same total is referred to as a magic square. Constructing a magic square of a given order is considered a difficult computational problem, particularly when ad... Read More about Constructing constrained-version of magic squares using selection hyper-heuristics.

Adaptive linear combination of heuristic orderings in constructing examination timetables (2014)
Journal Article
Abdul-Rahman, S., Bargiela, A., Burke, E., Özcan, E., McCollum, B., & McMullan, P. (2014). Adaptive linear combination of heuristic orderings in constructing examination timetables. European Journal of Operational Research, 232(2), https://doi.org/10.1016/j.ejor.2013.06.052

In this paper, we investigate adaptive linear combinations of graph coloring heuristics with a heuristic modifier to address the examination timetabling problem. We invoke a normalisation strategy for each parameter in order to generalise the specifi... Read More about Adaptive linear combination of heuristic orderings in constructing examination timetables.