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All Outputs (7)

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.