Investigation of Efficiency and Effectiveness of Hyper-Heuristic in NP-Hard Optimization
The problem representation defines the searching space in Evolutionary Computation (EC). Recent research
and developments of EC in optimisation show its robustness and capability of solving different types of
optimisation problem due to its stochastic characteristic. As all other computer intelligence system, EC
treats an optimisation problem as a searching problem, and the main objective is to find the optimal
parameters (solution), or combination of parameters for the problem, and EC simulates the process
of Biological Evolution, where each solution evolve/mutate/crossover to generate a better solution.
Traditional Evolutionary Algorithm only involve a single species (single searching space) in optimisation
process. In current co-evolutionary (both competitive and cooperative) methodology, a searching space
(single searching space) is further divided into sub space and the searching process is carried out in
a divide and conquer fashion.
In Co-evolving island models with different species, there are more than one species (more than one
searching space) involving in the optimisation process. The alternative searching space can be
theoretically derived, such as different colour space (RGB and HSI), mathematically transformed
(Fourier and Laplace Transform), dynamically evolve during the searching space, or even as
simple as rescaling the space domain.
These alternative spaces are then assisting the objective space in a cooperative co-evolution manner.
Such cooperative process could be as simple as a migration technique or as hard as a host-parasite
(solution from alternative space live on top of solution from objective space) relationship with
entropy test. There are still many design issues that are not investigated in this area.
- Cheng Wai Kheng, Chong Siang Yew, Bargiela Andrzej, Multi-resolution optimisation: Application of meta-heuristics in function re-modelling,
Proc. 23rd European Conference on Modelling and Simulation, ECMS 2009, Madrid, Spain, pp.834-840, June 2009, PDF
- Bargiela, A., Pedrycz, W., Granular computing: an introduction, Springer, 2003
- Bargiela, A., Pedrycz, W., (Eds.) Human-centric information processing through granular modelling, Studies in Computational Intelligence 182, Springer Berlin Heidelberg, 2009, (doi: 10.1007/978-3-540-92916-1)
- Bargiela A., Pedrycz W., Hirota K., Data granulation through optimization of similarity measure, Archives of Control Sciences, 12, 4, 469-491, 2002
- Pedrycz, W., Bargiela, A., Granular clustering: a granular signature of data, IEEE Trans. on Systems Man and Cybernetics, SMC-B, 32, 2, April 2002, 212-224, (doi: 10.1109/3477.990878 )
- Bargiela, A., Pedrycz, W., Recursive information granulation: Aggregation and interpretation issues, IEEE Trans. on Systems Man and Cybernetics SMC-B, 33, 1, 17, 2003, 96-112. (10.1109/TSMCB.2003.808190)
- Bargiela A., Pedrycz W., Hirota K., Granular prototyping in fuzzy clustering, IEEE Transactions on Fuzzy Systems, 12, 5, 2004, 697-709 (doi: 10.1109/TFUZZ.2004.834808)
- Bargiela, A., Pedrycz, W., Granular mappings, IEEE Transactions on Systems Man and Cybernetics SMC-A, vol. 35, 2, March 2005, 288-301 (doi: 10.1109/TSMCA.2005.843381)
- Bargiela, A., Pedrycz, W., A model of granular data: a design problem with the Tchebyschev FCM, Soft Computing, 9, 3, March 2005, 155-163 (doi: 10.1007/s00500-003-0339-2)
- Bargiela A., Homenda W., Information structuring in natural language communication Ð syntactical approach, Journal of Intelligent and Fuzzy Systems, 17(6), 2006, 575-582.
- Bargiela, A., Pedrycz, W., The roots of granular computing, Proceedings of 2006 IEEE International Conference on Granular Computing, 806-809
- Bargiela, A., Pedrycz, W., Toward a theory of Granular Computing for human-centred information processing, IEEE Trans. on Fuzzy Systems, vol. 16, 2, 2008, 320-330. (doi: 10.1109/TFUZZ.2007.905912)
- Pedrycz W., Bargiela A., Fuzzy clustering with semantically distinct families of variables: descriptive and predictive aspects, Pattern Recognition Letters, 31(13), 1952-1958, 2010. (doi: 10.1016/j.patrec.2010.06.016)
- Pedrycz, W., Bargiela, A., An Optimisation of allocation of information gramularity in the interpretation of data structures, IEEE Transactions on Systems Man and Cybernetics Part B , 42(3), 2012, 582-590 (doi: 10.1109/TSMCB.2011.2170067)
- Bargiela A., Pedrycz W., Optimised information abstraction in granular Min/Max clustering, in S. Ramanna, R.J. Howlett, L. Jain (ed.), Emerging Paradigms in Machine Learning, Springer, June 2012. ISBN 978-3-642-28698-8
- Bargiela A., Pedrycz W., Supervised and unsupervised information granulation: A study in hyperbox design, Chapter 3 in J.T.Yao (ed.), Novel developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, IGI Global, 2010.
- Bargiela, A., Pedrycz W., Self-organising maps for interactive information granulation, in Neural Network Applications in IT, In : D. Wang, N.K.Lee (eds.), University of Malasia Press, 1-19, 2004
- Bargiela, A. Pedrycz, W., Combined physical and mathematical model of Granular Computing, Proc. Int. Conf. On Soft Computing and Intelligent Systems SCIS2006, Tokyo, Japan, Sept. 2006
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