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)
Presentation / Conference Contribution
Gumus, D. B., Özcan, E., & Atkin, J. (2016, July). An investigation of tuning a memetic algorithm for cross-domain search. Presented at 2016 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada

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)
Presentation / Conference Contribution
Almutairi, A., Özcan, E., Kheiri, A., & Jackson, W. G. (2016, October). Performance of selection hyper-heuristics on the extended HyFlex domains. Presented at ISCIS: International Symposium on Computer and Information Sciences, Krakow, Poland

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)
Presentation / Conference Contribution
Gümüş, D. B., Özcan, E., & Atkin, J. (2016, October). An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget. Presented at ISCIS: International Symposium on Computer and Information Sciences, Krakow, Poland

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)
Presentation / Conference Contribution
Kheiri, A., Mısır, M., & Özcan, E. (2016, October). Ensemble move acceptance in selection hyper-heuristics. Presented at ISCIS: International Symposium on Computer and Information Sciences, Kraków, Poland

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)
Presentation / Conference Contribution
Jackson, W. G., Özcan, E., & John, R. I. (2016, July). A comparative study of fuzzy parameter control in a general purpose local search metaheuristic. Presented at 2016 IEEE Congress on Evolutionary Computation (CEC)

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)
Presentation / Conference Contribution
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (2016, July). Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic. Presented at The Genetic and Evolutionary Computation Conference (GECCO 2016), Denver, Colorado, USA

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.