Ender Özcan

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyperheuristic is relatively new; it was first used in(More)
Meta-heuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem in a certain domain. Hyper-heuristics introduce a novel approach for(More)
There is a growing body of work in the field of hyper-heuristics. Hyper-heuristics are high level search methodologies that operate on the space of heuristics to solve hard computational problems. A frequently used hyper-heuristic framework mixes a predefined set of low level heuristics during the search process. While most of the work on such selection(More)
http://dx.doi.org/10.1016/j.eswa.2013.12.050 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +44 7873729666, +966 506620227. E-mail addresses: psxmm3@exmail.nottingham.ac.uk, m.maashi@gmail.com (M. Maashi), ender.ozcan@nottingham.ac.uk (E. Özcan), graham.kendall@ nottingham.edu.my (G. Kendall). 1 Tel.: +6 (03) 8924 8306.(More)
The two dimensional orthogonal rectangular strip packing problem is a common NPhard optimisation problem whereby a set of rectangular shapes must be placed on a fixed width stock sheet with infinite length in such a way that wastage is minimised and material utilisation is maximised. The bidirectional best-fit heuristic is a deterministic approach which has(More)
Hyper-heuristics are proposed as a higher level of abstraction as compared to the metaheuristics. Hyper-heuristic methods deploy a set of simple heuristics and use only nonproblem-specific data, such as, fitness change or heuristic execution time. A typical iteration of a hyper-heuristic algorithm consists of two phases: heuristic selection method and move(More)
Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so(More)
Course timetabling problems are real world constraint optimization problems that are often coped with in educational institutions, such as universities or high schools. In this paper, we present a variety of new operators that can be also applied in evolutionary algorithms for other timetabling problems, such as, exam timetabling. Operators include(More)
Nurse rostering problems represent a subclass of scheduling problems that are hard to solve. The goal is finding high quality shift and resource assignments, satisfying the needs and requirements of employees as well as the employers in healthcare institutions. In this paper, a real case of a nurse rostering problem is introduced. Memetic Algorithms(More)