Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/15782
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Integrating neural networks and logistic regression to underpin hyper-heuristic search
Authors: Li, Jingpeng
Burke, Edmund
Qu, Rong
Contact Email: e.k.burke@stir.ac.uk
Keywords: Hyper-heuristic
Data mining
Educational timetabling
Neural network
Logistic regression
Issue Date: Mar-2011
Publisher: Elsevier
Citation: Li J, Burke E & Qu R (2011) Integrating neural networks and logistic regression to underpin hyper-heuristic search, Knowledge-Based Systems, 24 (2), pp. 322-330.
Abstract: A hyper-heuristic often represents a heuristic search method that operates over a space of heuristic rules. It can be thought of as a high level search methodology to choose lower level heuristics. Nearly 200 papers on hyper-heuristics have recently appeared in the literature. A common theme in this body of literature is an attempt to solve the problems in hand in the following way: at each decision point, first employ the chosen heuristic(s) to generate a solution, then calculate the objective value of the solution by taking into account all the constraints involved. However, empirical studies from our previous research have revealed that, under many circumstances, there is no need to carry out this costly 2-stage determination and evaluation at all times. This is because many problems in the real world are highly constrained with the characteristic that the regions of feasible solutions are rather scattered and small. Motivated by this observation and with the aim of making the hyper-heuristic search more efficient and more effective, this paper investigates two fundamentally different data mining techniques, namely artificial neural networks and binary logistic regression. By learning from examples, these techniques are able to find global patterns hidden in large data sets and achieve the goal of appropriately classifying the data. With the trained classification rules or estimated parameters, the performance (i.e. the worth of acceptance or rejection) of a resulting solution during the hyper-heuristic search can be predicted without the need to undertake the computationally expensive 2-stage of determination and calculation. We evaluate our approaches on the solutions (i.e. the sequences of heuristic rules) generated by a graph-based hyper-heuristic proposed for exam timetabling problems. Time complexity analysis demonstrates that the neural network and the logistic regression method can speed up the search significantly. We believe that our work sheds light on the development of more advanced knowledge-based decision support systems.
Type: Journal Article
URI: http://hdl.handle.net/1893/15782
DOI Link: http://dx.doi.org/10.1016/j.knosys.2010.10.004
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.
Affiliation: Computing Science and Mathematics
Deputy Principal's Office
University of Nottingham

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