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Adaptive terrain-based memetic algorithms

Published: 08 July 2009 Publication History

Abstract

The Terrain-Based Memetic Algorithm (TBMA) is a diffusion MA in which the local search (LS) behavior depends on the topological distribution of memetic material over a grid (terrain). In TBMA, the spreading of meme values -- such as LS step sizes -- emulates cultural differences which often arise in sparse populations. In this paper, adaptive capabilities of TBMAs are investigated by meme diffusion: individuals are allowed to move in the terrain and/or to affect their environment, by either following more effective memes or by transmitting successful meme values to nearby cells. In this regard, four TBMA versions are proposed and evaluated on three image vector quantizer design instances. The TBMAs are compared with K-Means and a Cellular MA. The results strongly indicate that utilizing dynamically adaptive meme evolution produces the best solutions using fewer fitness evaluations for this application.

References

[1]
]]E. Alba, B. Dorronsoro, and H. Alfonso. Cellular memetic algorithms. Journal of Computer Science and Technology, 5(4):257--263, December 2005.
[2]
]]E. Alba, A. J. Nebro, and J. M. Troya. Heterogeneous computing and parallel genetic algorithms. J. Parallel Distrib. Comput., 62(9):1362--1385, 2002.
[3]
]]C. R. Azevedo, T. A. Ferreira, W. T. Lopes, and F. Madeiro. Improving image vector quantization with a genetic accelerated k-means algorithm. In ACIVS '08: Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, pages 67--76, Berlin, Heidelberg, 2008. Springer--Verlag.
[4]
]]S. Baluja. Structure and performance of fine--grain parallelism in genetic search. In Proceedings of the 5th International Conference on Genetic Algorithms, pages 155--162, San Francisco, CA, USA, 1993. Morgan Kaufmann Publishers Inc.
[5]
]]T. Berger. Rate Distortion Theory: A Mathematical Basis for Data Compression. Prentice-Hall, Englewood Cliffs, NJ, 1971.
[6]
]]Y. K. Chiang and P. Tsai. Steganography using overlapping codebook partition. Signal Processing, 88(5):1203--1215, May 2008.
[7]
]]R. Dawkins. The Selfish Gene. Oxford University Press, New York, 1976.
[8]
]]B. Dorronsoro, D. Arias, F. Luna, A. J. Nebro, and E. Alba. A grid-based hybrid cellular genetic algorithm for very large scale instances of the CVRP. In W. W. Smari, editor, 2007 High Performance Computing & Simulation Conference (HPCS 2007), pages 759--765, 2007.
[9]
]]D. Eby, R. C. Averill, W. F. Punch, and E. D. Goodman. Optimal design of flywheels using an injection island genetic algorithm. AI EDAM, 13:327--340, 1999.
[10]
]]A. E. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Trans. Evolutionary Comp., 3(2):124--141, 1999.
[11]
]]P. Fränti, J. Kivijärvi, T. Kaukoranta, and O. Nevalainen. Genetic algorithm for codebook generation in vector quantization. In Proceedings of 3rd Nordic Workshop on Genetic Algorithms, pages 207--222, Helsinki, 1997.
[12]
]]M. Gen and R. Cheng. Genetic Algorithms and Engineering Optimization. Wiley-Interscience, Hoboken, 1999.
[13]
]]A. Gersho and R. M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston, 1992.
[14]
]]V. S. Gordon, R. Pirie, A. Wachter, and S. Sharp. Terrain-based genetic algorithm (TBGA): modeling parameter space as terrain. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99), pages 229--235, Orlando, FL, USA, 1999. Morgan Kaufmann Publishers Inc.
[15]
]]V. S. Gordon and L. D. Whitley. Serial and parallel genetic algorithms as function optimizers. In Proceedings of the 5th International Conference on Genetic Algorithms, pages 177--183, San Francisco, CA, USA, 1993. Morgan Kaufmann Publishers Inc.
[16]
]]V. S. Gordon and L. D. Whitley. A machine-independent analysis of parallel genetic algorithms. Journal of Complex Systems, 8(3):181--214, 1994.
[17]
]]J. He, L. Liu, and G. Palm. A discriminative training algorithm for VQ-based speaker identification. IEEE Trans. Speech Audio Process., 7(3):353--356, May 1999.
[18]
]]R. Hinterding, Z. Michalewicz, and A. E. Eiben. Adaptation in evolutionary computation: a survey. In Proceedings of the Fourth IEEE Conference on Evolutionary Computation, pages 65--69, Indianapolis, IN, 1997.
[19]
]]S. Janson, E. Alba, B. Dorronsoro, and M. Middendorf. Hierarchical cellular genetic algorithm. In Evolutionary Computation in Combinatorial Optimization EvoCOP06, pages 111--122, 2006.
[20]
]]N. Krasnogor and J. Smith. Emergence of profitable search strategies based on a simple inheritance mechanism. In International Genetic and Evolutionary Computation Conference (GECCO'01), pages 432--439, San Francisco, CA, 2001. Morgan Kaufmann Publishers Inc.
[21]
]]T. Krink and R. Ursem. Parameter control using the agent based patchwork model. In Proceedings of the Second Congress on Evolutionary Computation, pages 77--83, 2000.
[22]
]]D. Lee, S. Baek, and K. Sung. Modified K-means algorithm for vector quantizer design. IEEE Signal Processing Letters, 4(1):2-4, January 1997.
[23]
]]C.-C. Lin, S.-C. Chen, and N.-L. Hsueh. Adaptive embedding techniques for VQ-compressed images. Information Sciences, 179(1--2):140--149, January 2009.
[24]
]]Y. Linde, A. Buzo, and R. M. Gray. An algorithm for vector quantizer design. IEEE Trans. Commun., 28(1):84--95, January 1980.
[25]
]]P. Merz and B. Freisleben. A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In Proceedings of Congress on Evolutionary Computation, pages 2063--2070, IEEE Press, 1999.
[26]
]]P. Moscato. On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, Pasadena, CA, USA, 1989.
[27]
]]H. Muhlenbein and T. Mahnig. A comparison of stochastic local search and population based search. Proceedings of the Congress on Evolutionary Computation (CEC '02), 1:255--260, May 2002.
[28]
]]Q. H. Nguyen, Y. S. Ong, and M. H. Lim. Non-genetic transmission of memes by diffusion. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1017--1024, New York, NY, USA, 2008. ACM.
[29]
]]Y. S. Ong, M. H. Lim, N. Zhu, and K.-W. Wong. Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst, Man, Cybern. B, Cybern., 36(1):141--152, 2006.
[30]
]]K. K. Paliwal and V. Ramasubramanian. Comments on "modified k-means algorithm for vector quantizer design". IEEE Trans. Image Process., 9(11):1964--1967, 2000.
[31]
]]M. Ridley. Evolution. Blackwell Publishing, Oxford, 3 edition, 2004.
[32]
]]K. Sasazaki, S. Saga, J. Maeda, and Y. Suzuki. Vector quantization of images with variable block size. Applied Soft Computing, 8(1):634--645, 2008.
[33]
]]L. D. Whitley. Cellular genetic algorithms.In Proceedings of the 5th International Conference on Genetic Algorithms, page 658, San Francisco, CA, USA, 1993. Morgan Kaufmann Publishers Inc.
[34]
]]S. Wright. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In Proceedings of the 6th International Congress on Genetics, pages 356--366, 1932.
[35]
]]P. Yahampath and P. Rondeau. Multiple-description predictive-vector quantization with applications to low bit-rate speech coding over networks. IEEE Trans. Audio Speech Language Process., 15(3):749--755, March 2007.

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cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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Author Tags

  1. adaptation
  2. memetic algorithms
  3. terrain-based models

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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