All Outputs (14)

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.