Abstract
This paper proposes a metaheuristic approach to solve a complex large scale optimization problem that originates from a recently introduced Positron Emission Tomography (PET) data analysis method that provides an estimate of tissue heterogeneity. More specifically three modern metaheuristics have been tested. These metaheustics are based on Differential Evolution, Particle Swarm Optimization, and Memetic Computing. On the basis of a preliminary analysis of the fitness landscape, an intelligent initialization technique has been proposed in this paper. More specifically, since the fitness landscape appears to have a strong basin of attraction containing a multimodal landscape, a local search method is applied to one solution at the beginning of the optimization process and inserted into a randomly generated population. The resulting “doped” population is then processed by the metaheuristics. Numerical results show that the application of the local search at the beginning of the optimization process leads to significant benefits in terms of algorithmic performance. Among the metaheuristics analyzed in this study, the DE based algorithm appears to display the best performance.
This research is supported by the Academy of Finland, under the grant 213462 (Finnish Centre of Excellence Program (2006 - 2011)) and Akatemiatutkija 130600, Algorithmic Design Issues in Memetic Computing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bailey, D.: Positron emission tomography: basic sciences. Springer, Heidelberg (2005)
Barzilai, J., Borwein, J.M.: Two-point step size gradient methods. IMA Journal of Numerical Analysis 8(1), 141–148 (1988)
Barzilai, J., Borwein, J.M.: Two-Point Step Size Gradient Methods. IMA J. Numer. Anal. 8(1), 141–148 (1988)
Beylkin, G.: Discrete radon transform. IEEE Transactions on Acoustics, Speech and Signal Processing 35(2), 162–172 (1987)
Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)
Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of pmsm drives. IEEE Transactions on System Man and Cybernetics-part B 37(1), 28–41 (2007)
Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Computing-A Fusion of Foundations, Methodologies and Applications 13(8), 811–831 (2009)
Hart, W.E., Krasnogor, N., Smith, J.E.: Memetic evolutionary algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms, pp. 3–27. Springer, Berlin (2004)
Hubbard, W.: The approximation of a poisson distribution by a gaussian distribution. Proceedings of the IEEE 58(9), 1374 (1970)
Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, Los Alamitos (1988)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10(3), 281–295 (2006)
Neri, F., Tirronen, V.: Recent advances in differential evolution: A review and experimental analysis. Artificial Intelligence Review 33(1), 61–106 (2010)
Neri, F., Toivanen, J., Cascella, G.L., Ong, Y.S.: An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(2), 264–278 (2007)
Ohkura, K., Matsumura, Y., Ueda, K.: Robust evolution strategies. Applied Intelligence 15(3), 153–169 (2001)
O’Sullivan, F., Roy, S., Eary, J.: A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics 4(3), 433–438 (2003)
Peña-Reyes, C.A., Sipper, M.: Evolutionary computation in medicine: an overview. Artificial Intelligence in Medicine 19, 1–23 (2000)
Pölönen, H., Niemi, J., Ruotsalainen, U.: Error-corrected estimation of regional kinetic parameter histograms directly from pet projections. Physics in Medicine and Biology 55(24) (2010)
Raydan, M.: On the Barzilai and Borwein choice of steplength for the gradient method. IMA Journal of Numerical Analysis 13(3), 321–326 (1993)
Reivich, M., Kuhl, D., Wolf, A., Greenberg, J., Phelps, M., Ido, T., Casella, V., Fowler, J., Hoffman, E., Alavi, A., Som, P., Sokoloff, L.: The [18f]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circ. Res. 44(1), 127–137 (1979)
Tseng, L.Y., Chen, C.: Multiple trajectory search for large scale global optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 3052–3059 (2008)
Valli, G., Poli, R., Cagnoni, S., Coppini, G.: Neural networks and prior knowledge help the segmentation of medical images. Journal of Computing and Information Technology 6(2), 117–133 (1998)
Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)
Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pekkarinen, J., Pölönen, H., Neri, F. (2011). Advanced Metaheuristic Approaches and Population Doping for a Novel Modeling-Based Method of Positron Emission Tomography Data Analysis. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-20525-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20524-8
Online ISBN: 978-3-642-20525-5
eBook Packages: Computer ScienceComputer Science (R0)