Abstract
Recently, a Genetic Accelerated K-Means Algorithm (GAKM) was proposed as an approach for optimizing Vector Quantization (VQ) codebooks, relying on an accelerated version of K-Means algorithm as a new local learning module. This approach requires the determination of a scale factor parameter (η), which affects the local search performed by GAKM. The problem of auto-adapting the local search in GAKM, by adjusting the η parameter, is addressed in this work by the proposal of a Terrain-Based Memetic Algorithm (TBMA), derived from existing spatially distributed evolutionary models. Simulation results regarding image VQ show that this new approach is able to adjust the scale factor (η) for different images at distinct coding rates, leading to better Peak Signal-to-Noise Ratio values for the reconstructed images when compared to both K-Means and Cellular Genetic Algorithm + K-Means. The TBMA also demonstrates capability of tuning the mutation rate throughout the genetic search.
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References
Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Operations Research/Computer Science Interfaces, vol. 42. Springer, Heidelberg (2008)
Alba, E., Dorronsoro, B.: A hybrid genetic algorithm for the capacited vehicle routing problem. In: Abraham, A., Grosan, C., Pedrycz, W. (eds.) Egineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol. 82, pp. 379–422. Springer, Heidelberg (2008)
Andre, J., Siarry, P., Dognon, T.: An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization. Adv. Eng. Softw. 32(1), 49–60 (2000)
Azevedo, C.R.B., Ferreira, T.A.E., Lopes, W.T.A., Madeiro, F.: Improving image vector quantization with a genetic accelerated k-means algorithm. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 67–76. Springer, Heidelberg (2008)
Cosman, P.C., Gray, R.M., Vetterli, M.: Vector quantization of image subbands: a survey. IEEE Trans. Image Process. 5(2), 202–225 (1996)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evolutionary Comp. 3(2), 124–141 (1999)
Fränti, P.: Genetic algorithm with deterministic crossover for vector quantization. Pattern Recog. Lett. 21, 61–68 (2000)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer, Boston (1992)
Gordon, V.S., Pirie, R., Wachter, A., Sharp, S.: Terrain-based genetic algorithm (TBGA): modeling parameter space as terrain. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M., Honavar, V., Jakiela, M., Smith, R. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999, pp. 229–235. Morgan Kaufmann, San Francisco (1999)
Krasnogor, N., Smith, J.E.: A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Trans. Evolutionary Comp. 9(5), 474–488 (2005)
Krasnogor, N., Smith, J.E.: Emergence of profitable search strategies based on a simple inheritance mechanism. In: Spector, L., Goodman, E., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 432–439. Morgan Kaufmann, San Francisco (2001)
Krink, T., Mayoh, B.H., Michalewicz, Z.: A patchwork model for evolutionary algorithms with structured and variable size populations. In: Banzhaf, W., Daida, J.M., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M.J., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 1999, pp. 1321–1328. Morgan Kaufmann, San Francisco (1999)
Krink, T., Ursem, R.K.: Parameter control using the agent based patchwork model. In: Fonseca, C., Kim, J.-H., Smith, A. (eds.) Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2000, pp. 77–83. IEEE Press, New York (2000)
Krishna, K., Murty, M.: Genetic k-means algorithm. IEEE Trans. Syst., Man, Cybern. 29(3), 433–439 (1999)
Laskaris, N.A., Fotopoulos, S.: A novel training scheme for neural-network-based vector quantizers and its application in image compression. Neurocomp 61, 421–427 (2004)
Lee, D., Baek, S., Sung, K.: Modified k-means algorithm for vector quantizer design. IEEE Signal Process. Lett. 4(1), 2–4 (1997)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)
Ong, Y.-S., Lim, M.-H., Zhu, N., Wong, K.-W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst., Man, Cybern. 36(1), 141–152 (2005)
Schlierkamp-Voosen, D., Muhlenbein, H.: Adaptation of population sizes by competing subpopulations. In: Proceedings of IEEE International Conference on Evolutionary Computation, CEC 1996, pp. 330–335. IEEE Press, New York (1996)
Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst., Man, Cybern. 36(1), 17–26 (1994)
Ursem, R.K.: Models for Evolutionary Algorithms and Their Application in System Identification and Control Optimization. PhD Dissertation, University of Aarhus. Denmark (2003)
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Azevedo, C.R.B., Azevedo, F.E.A.G., Lopes, W.T.A., Madeiro, F. (2009). Terrain-Based Memetic Algorithms for Vector Quantizer Design. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_17
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DOI: https://doi.org/10.1007/978-3-642-03211-0_17
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