STORRE
STORRE: Stirling Online Research Repository
http://hdl.handle.net/1893/15124| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Peer Review Status: | Refereed |
| Author(s): | Ochoa, Gabriela Walker, James Hyde, Matthew Curtois, Tim |
| Contact Email: | gabriela.ochoa@stir.ac.uk |
| Title: | Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework |
| Editor(s): | Coello, Coello CA Cutello, V Deb K, K Forrest, S Nicosia, G Pavone, M |
| Citation: | Ochoa G, Walker J, Hyde M & Curtois T (2012) Adaptive evolutionary algorithms and extensions to the HyFlex hyper-heuristic framework. In: Coello CC, Cutello V, Deb K K, Forrest S, Nicosia G & Pavone M (eds.) Parallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 7492. 12th International Conference, Taormina, Italy, 01.09.2012-05.09.2012. Amsterdam: Springer, pp. 418-427. http://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_42# |
| Issue Date: | 2012 |
| Date Deposited: | 6-Jun-2013 |
| Series/Report no.: | Lecture Notes in Computer Science, Vol. 7492 |
| Conference Name: | 12th International Conference |
| Conference Dates: | 2012-09-01 - 2012-09-05 |
| Conference Location: | Taormina, Italy |
| Abstract: | HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem. |
| Status: | VoR - Version of Record |
| Rights: | The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. |
| URL: | http://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_42# |
| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Adaptive Evolutionary Algorithms.pdf | Fulltext - Published Version | 186.24 kB | Adobe PDF | Under Embargo until 3000-08-31 Request a copy |
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright |
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.