Browse by author
Lookup NU author(s): Professor Natalio KrasnogorORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
The ultimate aim of Memetic Computing is the fully autonomous solution to complex optimisation problems. For a while now, the Memetic algorithms literature has been moving in the direction of ever increasing generalisation of optimisers initiated by seminal papers such as Krasnogor and Smith (IEEE Trans 9(5): 474-488, 2005; Workshops Proceedings of the 2000 International Genetic and Evolutionary Computation Conference (GECCO2000), 2000), Krasnogor and Gustafson (Advances in nature-inspired computation: the PPSN VII Workshops 16(52), 2002) and followed by related and more recent work such as Ong and Keane (IEEE Trans Evol Comput 8(2): 99-110, 2004), Ong et al. (IEEE Comp Int Mag 5(2): 24-31, 2010), Burke et al. (Hyper-heuristics: an emerging direction in modern search technology, 2003). In this recent trend to ever greater generalisation and applicability, the research has focused on selecting (or even evolving), the right search operator(s) to use when tackling a given instance of a fixed problem type (e. g. Euclidean 2D TSP) within a range of optimisation frameworks (Krasnogor, Handbook of natural computation, Springer, Berlin/Heidelberg, 2009). This paper is the first step up the generalisation ladder, where one assumes that the optimiser is given (perhaps by other solvers who do not necessarily know how to deal with a given problem instance) a problem instance to tackle and it must autonomously and without human intervention pre-select which is the likely family class of problems the instance belongs to. In order to do that we propose an Automatic Problem Classifier System able to identify automatically which kind of instance or problem the system is dealing with. We test an innovative approach to the Universal Similarity Metric, as a variant of the normalised compression distance (NCD), to classify different problem instances. This version is based on the management of compression dictionaries. The results obtained are encouraging as we achieve a 96% average classification success with the studied dataset.
Author(s): Contreras I, Arnaldo I, Krasnogor N, Hidalgo JI
Publication type: Article
Publication status: Published
Journal: Memetic Computing
Year: 2014
Volume: 6
Issue: 4
Pages: 263-276
Print publication date: 01/12/2014
Online publication date: 08/11/2014
Acceptance date: 13/10/2014
ISSN (print): 1865-9284
ISSN (electronic): 1865-9292
Publisher: Springer
URL: http://dx.doi.org/10.1007/s12293-014-0145-7
DOI: 10.1007/s12293-014-0145-7
Altmetrics provided by Altmetric
