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The Fast Evaluation Strategy for Evolvable Hardware

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Abstract

An evolutionary algorithm implemented in hardware is expected to operate much faster than the equivalent software implementation. However, this may not be true for slow fitness evaluation applications. This paper introduces a fast evolutionary algorithm (FEA) that does not evaluate all new individuals, thus operating faster for slow fitness evaluation applications. Results of a hardware implementation of this algorithm are presented that show the real time advantages of such systems for slow fitness evaluation applications. Results are presented for six optimisation functions and for image compression hardware.

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References

  1. T. Bäck, D. Fogel, and Z. Michalewicz Handbook of Evolutionary Computation, Institute of Physics Publishing Ltd., Bristol and Oxford University Press: New York, 1997.

    Google Scholar 

  2. T. Blickle and L. Thiele “A comparison of selection schemes used in evolutionary algorithms,” Evolutionary Computation, vol. 4, no. 4, pp. 361–394, 1996.

    Google Scholar 

  3. L. Booker, D. E. Goldberg, and J. H. Holland “Classifier systems and genetic algorithms,” Artificial Intelligence, vol. 40, nos. 1–3, pp. 235–282, 1989.

    Google Scholar 

  4. E. Cantú-Paz Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers: Boston, MA, 2000.

    Google Scholar 

  5. M. El-Beltagy and A. Keane “Using self organizing maps and genetic algorithms for model selection in multilevel optimization,” in Proceedings of the 12th International Conference on Industrial Applications and Intelligence and Expert Systems, I. Imam Y. Kodratoff, A. El-Dessouki, and M. Ali (Eds.) Lecture Notes in Computer Science 1611, Springer-Verlag: Berlin, pp. 137–144, 1999.

    Google Scholar 

  6. A. E. Eiben, R. Hinterding, and Z. Michalewicz “Parameter control in evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 124–141, 1999.

    Google Scholar 

  7. U. Hammel and T. Bäck “Evolution strategies on noisy functions: How to improve convergence properties,” in Parallel Problem Solving from Nature (PPSN III), Y. Davidor, H.-P. Schwefel, and R. Manner (Eds.) Lecture Notes in Computer Science 866, Springer-Verlag: Berlin, 1994, pp. 159–168.

    Google Scholar 

  8. T. Higuchi and N. Kajihara “Evolvable hardware chips for industrial applications,” Communications of the ACM, vol. 42, no. 4, pp. 60–66, 1999.

    Google Scholar 

  9. J. H. Holland Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence, 2nd edition, MIT Press: Cambridge, MA, 1992.

    Google Scholar 

  10. J. Marin and R. V. Sole “Macroevolutionary algorithms: A new optimization method on fitness landscape,” IEEE Transaction on Evolutionary Computation, vol. 3, no. 4, pp. 272–286, 1999.

    Google Scholar 

  11. S. K. Pal and P. P. Wang Genetic Algorithms and Pattern Recognition, CRC Press: Florida, 1996.

    Google Scholar 

  12. M. J. D. Powell “Direct search algorithms for optimization calculations,” Acta Numerica, vol. 7, pp. 288-336. Cambridge University Press: Cambridge, 1998.

  13. S. Rana, L. D. Whitley, and R. Cogswell “Searching in the presence of noise,” in Parallel Problem Solving from Nature (PPSN IV), H. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (Eds.) Lecture Notes in Computer Science 1141, Springer-Verlag: Berlin, 1996, pp. 198–207.

    Google Scholar 

  14. A. Ratle “Optimal sampling strategies for learning a fitness model,” In Proceedings of the 1999 Congress on Evolutionary Computation, P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and Z. Ali (Eds.) vol. 3, pp. 2078–2085, IEEE Press: Piscataway, NJ, 1999.

  15. M. Salami “Genetic algorithm processor on reprogrammable architectures,” In Proceedings of the Evolutionary Programming 1996 (EP96), L. J. Fogel, P. J. Angeline, and T. Bäck (Eds.) MIT Press: Cambridge, MA, 1996, pp. 355–361.

  16. M. Salami and T. Hendtlass “A fitness estimation strategy for Genetic algorithms,” in Proceedings of the Fifteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE), T. Hendtlass and M. Ali (Eds.) Lecture Notes in Computer Science 2358, Springer-Verlag, Berlin, 2002, pp. 502–513.

  17. M. Salami and T. Hendtlass “A fast evolutionary algorithm for image compression in hardware,” in Proceedings of the Fifteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE), T. Hendtlass and M. Ali (Eds.) Lecture Notes in Computer Science 2358, Springer-Verlag: Berlin, 2002, pp. 241–252.

  18. M. Salami and T. Hendtlass “A fast evaluation strategy for evolutionary algorithms,” Applied Soft Computing, vol. 2/3F, pp. 156–173, 2003.

    Google Scholar 

  19. M. Salami, H. Sakanashi, M. Iwata, and T. Higuchi “On-line compression of high precision printer images by evolvable hardware,” in The Proceedings of The 1998 Data Compression Conference (DCC98), J. A. Storer and M. Cohn (Eds.) IEEE Computer Society Press: Los Alamitos, CA, USA, 1998.

  20. K. Sastry, D. E. Goldberg, and M. Pelikan “Don’t evaluate, inherit,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), L. Spector, E. D. Goodman, A. Wu, W. B. Langdon, H.-M. Voigt, M. GenS. Sen, M. Dorigo, S. Pezeshk, M. H. Garzon, and E. Burke(Eds.)pp. 551–558, Morgan Kaufmann: San Francisco, California, USA, 2001.

  21. B. Shackleford, G. Snider, R. J. Carter, E. Okushi, M. Yasuda, K. Seo, and H. Yasuura “A high-performance, pipelined, FPGA-based genetic algorithm machine,” Genetic Programming and Evolvable Hardware, vol. 2, no. 1, pp. 33–60, 2001.

    Google Scholar 

  22. R. Smith, B. Dike, and S. Stegmann “Fitness inheritance in genetic algorithms,” in Proceedings of the ACM Symposium on Applied Computing, K. George, J. Carrol, E. Deaton, D. Oppenheim, and J. Hightower (Eds.) ACM Press: New York, 1995, pp. 345–350.

  23. M. Weinberger, G. Seroussi, and G. Sapiro “The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS,” IEEE Transactions on Image Processing, vol. 9, pp. 1309–1324, 2000.

    Google Scholar 

  24. D. Whitley, S. Rana, and R. B. Heckendorn “Island model genetic algorithms and linearly separable problems,” in Proceedings of the AISB Workshop on Evolutionary Computation, D. Corne and J. L. Shapiro (Eds.) Lecture Notes in Computer Science 1305, Springer-Verlag: Berlin, 1997, pp. 109–125.

  25. X. Yao and Y. Liu “Fast evolution strategies,” Control and Cybernetics, vol. 26, no. 3, pp. 467–496, 1997.

    Google Scholar 

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Authors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, School of Information Technology, Swinburne University of Technology, P.O. Box 218, Hawthorn, Australia, 3122

    Mehrdad Salami & Tim Hendtlass

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  1. Mehrdad Salami
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  2. Tim Hendtlass
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Correspondence to Mehrdad Salami.

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Salami, M., Hendtlass, T. The Fast Evaluation Strategy for Evolvable Hardware. Genet Program Evolvable Mach 6, 139–162 (2005). https://doi.org/10.1007/s10710-005-7578-1

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