An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic
Heuristic methodologies appears for solving optimisation problems. Hyper-heuristics focus on search spaces to select or generate the suitable low-level heuristics to solve computationally difficult problems rather than focusing on finding solutions directly. The main goal is to develop more general...
| Main Author: | |
|---|---|
| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2015
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/30793/ |
| _version_ | 1848794061320749056 |
|---|---|
| author | Qarout, Rehab |
| author_facet | Qarout, Rehab |
| author_sort | Qarout, Rehab |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Heuristic methodologies appears for solving optimisation problems. Hyper-heuristics focus on search spaces to select or generate the suitable low-level heuristics to solve computationally difficult problems rather than focusing on finding solutions directly.
The main goal is to develop more generally applicable search methodologies. The selection hyper-heuristics will be the core of designing the proposed algorithm and consist of two stages: the selection stage of low-level heuristics, and the acceptance stage of the solutions. An Evolutionary Algorithm approach produces high quality hyper-heuristics which can find optimal solutions for optimisation problems effectively. The Memetic Algorithms are evolutionary intelligent algorithms combining Genetic Algorithm with local search components. A Multi-Meme Memetic Algorithm presented in this project as a population based search method with Choice Function as a selection mechanism for low-level heuristics. The selection mechanism is encoded by multi-meme self-adaptation strategy for automating tuning of the choice function parameters. For each individual in the population, a meme encodes which setting is the best for Choice Function parameters for each operator type and relevant parameters of a chosen operator. Multi-Meme strategy is considered as a self-adaptive mechanism using a reward points system to increase the score for the meme that shows local improvement and uses these scores in the selection process. The proposed hyper-heuristics is tested and compared with the performance of previous hyper-heuristics which competed in the CHeSC2011 challenge across 9 problem domains. The achieved result was remarkable in some problem domains and opens some scope for further improvement in the proposed hyper-heuristic to improve the result in the rest of the problem domains. |
| first_indexed | 2025-11-14T19:10:12Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-30793 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:10:12Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-307932017-10-19T15:05:49Z https://eprints.nottingham.ac.uk/30793/ An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic Qarout, Rehab Heuristic methodologies appears for solving optimisation problems. Hyper-heuristics focus on search spaces to select or generate the suitable low-level heuristics to solve computationally difficult problems rather than focusing on finding solutions directly. The main goal is to develop more generally applicable search methodologies. The selection hyper-heuristics will be the core of designing the proposed algorithm and consist of two stages: the selection stage of low-level heuristics, and the acceptance stage of the solutions. An Evolutionary Algorithm approach produces high quality hyper-heuristics which can find optimal solutions for optimisation problems effectively. The Memetic Algorithms are evolutionary intelligent algorithms combining Genetic Algorithm with local search components. A Multi-Meme Memetic Algorithm presented in this project as a population based search method with Choice Function as a selection mechanism for low-level heuristics. The selection mechanism is encoded by multi-meme self-adaptation strategy for automating tuning of the choice function parameters. For each individual in the population, a meme encodes which setting is the best for Choice Function parameters for each operator type and relevant parameters of a chosen operator. Multi-Meme strategy is considered as a self-adaptive mechanism using a reward points system to increase the score for the meme that shows local improvement and uses these scores in the selection process. The proposed hyper-heuristics is tested and compared with the performance of previous hyper-heuristics which competed in the CHeSC2011 challenge across 9 problem domains. The achieved result was remarkable in some problem domains and opens some scope for further improvement in the proposed hyper-heuristic to improve the result in the rest of the problem domains. 2015-12-10 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/30793/1/Rehab_Qarout_4201424.pdf Qarout, Rehab (2015) An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic. [Dissertation (University of Nottingham only)] Hyper-heuristics Self-Adaptation Multi-Meme Memetic Algorithm Choice Function Heuristic Selection Cross-domain Optimisation. |
| spellingShingle | Hyper-heuristics Self-Adaptation Multi-Meme Memetic Algorithm Choice Function Heuristic Selection Cross-domain Optimisation. Qarout, Rehab An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title | An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title_full | An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title_fullStr | An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title_full_unstemmed | An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title_short | An adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| title_sort | adaptive multi meme memetic algorithm embedding choice function hyper-heuristic |
| topic | Hyper-heuristics Self-Adaptation Multi-Meme Memetic Algorithm Choice Function Heuristic Selection Cross-domain Optimisation. |
| url | https://eprints.nottingham.ac.uk/30793/ |