Professor Ender Ozcan's Outputs (116)

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.

Modified choice function heuristic selection for the Multidimensional Knapsack Problem (2014)
Presentation / Conference Contribution
Drake, J. H., Özcan, E., & Burke, E. K. (2014, October). Modified choice function heuristic selection for the Multidimensional Knapsack Problem. Presented at Eighth International Conference on Genetic and Evolutionary Computing, Nanchang, China

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)
Presentation / Conference Contribution
Turk, S., John, R., & Özcan, E. Interval type-2 fuzzy sets in supplier selection. Presented at 2014 14th UK Workshop on Computational Intelligence (UKCI)

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)
Presentation / Conference Contribution
Jackson, W. G., Özcan, E., & John, R. I. (2014, September). Fuzzy adaptive parameter control of a late acceptance hyper-heuristic. Presented at 14th UK Workshop on Computational Intelligence UKCI2014

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.

Preface (2014)
Presentation / Conference Contribution
(2014, August). Preface. Presented at 10th International Conference on the Practice and Theory of Automated Timetabling, York

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.

An ant-based selection hyper-heuristic for dynamic environments (2013)
Presentation / Conference Contribution
Kiraz, B., Etaner-Uyar, A. Ş., & Özcan, E. (2013, April). An ant-based selection hyper-heuristic for dynamic environments. Presented at 16th European Conference on Applications of Evolutionary Computing, Vienna, Austria

Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and... Read More about An ant-based selection hyper-heuristic for dynamic environments.

Partial shape matching using genetic algorithms (1997)
Journal Article
Ozcan, E., & Mohan, C. K. (1997). Partial shape matching using genetic algorithms. Pattern Recognition Letters, 18(10), 987-992. https://doi.org/10.1016/S0167-8655%2897%2900123-2

Shape recognition is a challenging task when images contain overlapping, noisy, occluded, partial shapes. This paper addresses the task of matching input shapes with model shapes described in terms of features such as line segments and angles. The qu... Read More about Partial shape matching using genetic algorithms.