Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/15750
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Drake, John
Ozcan, Ender
Burke, Edmund
Contact Email: e.k.burke@stir.ac.uk
Title: An improved choice function heuristic selection for cross domain heuristic search
Editors: Coello, Coello CA
Cutello, V
Deb, K
Forrest, S
Nicosia, G
Pavone, M
Citation: Drake J, Ozcan E & Burke E (2012) An improved choice function heuristic selection for cross domain heuristic search In: Coello Coello CA, Cutello V, Deb K, Forrest S, Nicosia G, Pavone M (ed.) Parallel Problem Solving from Nature - PPSN XII , Berlin Heidelberg: Springer. 12th International Conference on Parallel Problem Solving from Nature - PPSN XII, 1.9.2012 - 5.9.2012, Taormina, Italy, pp. 307-316.
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science, 7492
Conference Name: 12th International Conference on Parallel Problem Solving from Nature - PPSN XII
Conference Dates: 2012-09-01T00:00:00Z
Conference Location: Taormina, Italy
Abstract: Hyper-heuristics are a class of high-level search technologies to solve computationally difficult problems which operate on a search space of low-level heuristics rather than solutions directly. A iterative selection hyper-heuristic framework based on single-point search relies on two key components, a heuristic selection method and a move acceptance criteria. The Choice Function is an elegant heuristic selection method which scores heuristics based on a combination of three different measures and applies the heuristic with the highest rank at each given step. Each measure is weighted appropriately to provide balance between intensification and diversification during the heuristic search process. Choosing the right parameter values to weight these measures is not a trivial process and a small number of methods have been proposed in the literature. In this study we describe a new method, inspired by reinforcement learning, which controls these parameters automatically. The proposed method is tested and compared to previous approaches over a standard benchmark across six problem domains.
Type: Conference Paper
Status: Book Chapter: publisher version
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.
URI: http://hdl.handle.net/1893/15750
URL: http://link.springer.com/chapter/10.1007%2F978-3-642-32964-7_31
Affiliation: University of Nottingham
University of Nottingham
Deputy Principal's Office

Files in This Item:
File Description SizeFormat 
An improved choice function heuristic selection for cross domain heuristic search.pdf203.97 kBAdobe PDFUnder Embargo until 31/12/2999     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 dependant 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.

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.