All Outputs (6)

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.

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.