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The Advantages of Landscape Neutrality in Digital Circuit Evolution

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Evolvable Systems: From Biology to Hardware (ICES 2000)

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Abstract

The paper studies the role of neutrality in the fitness landscapes associated with the evolutionary design of digital circuits and particularly the three-bit binary multiplier. For the purpose of the study, digital circuits are evolved extrinsically on an array of logic cells. To evolve on an array of cells, a genotype-phenotype mapping has been devised by which neutrality can be embedded in the resulting fitness landscape. It is argued that landscape neutrality is beneficial for digital circuit evolution.

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References

  1. Kajitani, I., Hushino, T., Nishikawa, D., Yokoi, H., Nakaya, S., Yamauchi, T., Inuo, T., Kajihara, N., Iwata, M., Keymeulen, D., Higuchi, T.: A gate-level ehw chip: Implementing GA operations and reconfigurable hardware on a single LSI. In Sipper, M., Mange, D., Pérez-Uribe, A., eds.: Proceedings of the 2nd International Conference on Evolvable Systems: From Biology to Hardware. Heidelberg, Springer-Verlag (1998) 1–12.

    Chapter  Google Scholar 

  2. Higuchi, T., Niwa, T., Tanaka, T., Iba, H., de Garis, H., Furuya, T.: Evolving hardware with genetic learning: A first step towards building a Darwin machine. In Meyer, J.A., Roitblat, H.L., Stewart, W., eds.: From Animals to Animats II: Proceedings of the 2nd International Conference on Simulation of Adaptive Behaviour. Cambridge, MA, MIT Press (1993) 417–424.

    Google Scholar 

  3. Hemmi, H., Hikage, T., Shimohara, K.: Adam: A hardware evolutionary system. In: Proceedings of the 1st International Conference on Evolutionary Computation. Piscataway, NJ, IEEE press (1994) 193–196.

    Google Scholar 

  4. Hemmi, H., Mizoguchi, J., Shimohara, K.: Development and evolution of hardware behaviours. In Brooks, R., Maes, P., eds.: Artificial Life IV: Proceedings of the 4th International Workshop on the Synthesis and Simulation of Living Systems. Cambridge, MA, MIT Press (1994) 371–376.

    Google Scholar 

  5. Miller, J.F., Thomson, P., Fogarty, T.: Designing electronic circuits using evolutionary algorithms. arithmetic circuits: A case study. In Quagliarella, D., Periaux, J., Poloni, C., Winter, G., eds.: Genetic Algorithms and Evolution Strategies in Engineering and Computer Science. Wiley, Chechester, UK (1997) 105–131.

    Google Scholar 

  6. Iba, H., Iwata, M., Higuchi, T.: Machine learning approach to gate-level evolvable hardware. In Higuchi, T., Iwata, M., eds.: Proceedings of the 1st International Conference on Evolvable Systems: From Biology to Hardware. Heidelberg, Springer-Verlag (1997) 327–343.

    Google Scholar 

  7. Vassilev, V.K., Miller, J.F., Fogarty, T.C.: Digital circuit evolution and fitness landscapes. In: Proceedings of the Congress on Evolutionary Computation. Volume 2., Piscataway, NJ, IEEE Press (1999) 1299–1306.

    Google Scholar 

  8. Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In Jones, D.F., ed.: Proceedings of the 6th International Conference on Genetics. Volume 1. (1932) 356–366.

    Google Scholar 

  9. Kauffman, S.A.: Adaptation on rugged fitness landscapes. In Stein, D., ed.: Lectures in the Sciences of Complexity. SFI Studies in the Sciences of Complexity. Addison-Wesley, Reading, MA (1989) 527–618.

    Google Scholar 

  10. Manderick, B., de Weger, M., Spiessens, P.: The genetic algorithm and the structure of the fitness landscape. In Belew, R.K., Booker, L.B., eds.: Proceedings of the 4th International Conference on Genetic Algorithms. San Mateo, CA, Morgan Kaufmann (1991) 143–150.

    Google Scholar 

  11. Mitchell, M., Forrest, S., Holland, J.: The royal road for genetic algorithms: Fitness landscapes and ga performance. In Varela, J., Bourgine, P., eds.: Proceedings of the 1st European Conference on Artificial Life. Cambridge, MA, MIT Press (1991) 245–254.

    Google Scholar 

  12. Palmer, R.: Optimization on rugged landscapes. In Perelson, A., Kauffman, S., eds.: Molecular Evolution on Rugged Landscapes. Volume IX of SFI Studies in the Sciences of Complexity. Addison-Wesley, Reading, MA (1991) 3–25.

    Google Scholar 

  13. Wagner, G.P., Altenberg, L.: Complex adaptations and the evolution of evolvability. Evolution 50 (1995) 967–976.

    Article  Google Scholar 

  14. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1997) 67–82.

    Article  Google Scholar 

  15. Stadler, P.F., Seitz, R., Wagner, G.P.: Evolvability of complex characters: Dependent fourier decomposition of fitness landscapes over recombination spaces. Technical Report 99-01-001, Santa Fe Institute (1999).

    Google Scholar 

  16. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application. In Ghosh, A., Tsutsui, S., eds.: Theory and Application of Evolutionary Computation: Recent Trends. Springer-Verlag, London (2000) In press.

    Google Scholar 

  17. Miller, J.F., Job, D., Vassilev, V.K.: Principles in the evolutionary design of digital circuits. Journal of Genetic Programming and Evolvable Machines 1 (2000).

    Google Scholar 

  18. Reidys, C.M., Stadler, P.F.: Neutrality in fitness landscapes. Technical Report 98-10-089, Santa Fe Institute (1998).

    Google Scholar 

  19. Stadler, P.F.: Spectral landscape theory. In Crutchfield, J.P., Schuster, P., eds.: Evolutionary Dynamics — Exploring the Interplay of Selection, Neutrality, Accident and Function. Oxford University Press, New York (1999).

    Google Scholar 

  20. Kimura, M.: Evolutionary rate at the molecular level. Nature 217 (1968) 624–626.

    Article  Google Scholar 

  21. King, J.L., Jukes, T.H.: Non-darwinian evolution. Science 164 (1969) 788–798.

    Article  Google Scholar 

  22. Ohta, T.: Slightly deleterious mutant substitutions in evolution. Nature 246 (1973) 96–97.

    Article  Google Scholar 

  23. Ohta, T.: The nearly neutral theory of molecular evolution. Annual Review of Ecology and Systematics 23 (1992) 263–286.

    Article  Google Scholar 

  24. Huynen, M.A., Stadler, P.F., Fontana, W.: Smoothness within ruggedness: The role of neutrality in adaptation. Proceedings of the National Academy of Science U.S.A. 93 (1996) 397–401.

    Article  Google Scholar 

  25. Huynen, M.A.: Exploring phenotype space through neutral evolution. Journal of Molecular Evolution 43 (1996) 165–169.

    Article  Google Scholar 

  26. Banzhaf, W.: Genotype-phenotype-mapping and neutral variation — a case study in genetic programming. In Davidor, Y., Schwefel, H.P., Männer, R., eds.: Parallel Problem Solving from Nature III. Berlin, Springer-Verlag (1994) 322–332.

    Google Scholar 

  27. Harvey, I., Thompson, A.: Through the labyrinth evolution finds a way: A silicon ridge. In Higuchi, T., Iwata, M., Liu, W., eds.: Proceedings of the 1st International Conference on Evolvable Systems. Berlin, Springer-Verlag (1996) 406–422.

    Google Scholar 

  28. Miller, J.F.: An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E., eds.: Proceedings of the 1st Genetic and Evolutionary Computation Conference. Volume 2., San Francisco, CA, Morgan Kaufmann (1999) 1135–1142.

    Google Scholar 

  29. Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Proceedings of the 3rd European Conference on Genetic Programming. Berlin, Springer-Verlag (2000).

    Google Scholar 

  30. Schwefel, H.P.: Numerical Optimization of Computer Models. John Wiley & Sons, Chichester, UK (1981).

    MATH  Google Scholar 

  31. Bäck, T., Hoffmeister, F., Schwefel, H.P.: A survey of evolutionary strategies. In Belew, R., Booker, L., eds.: Proceedings of the 4th International Conference on Genetic Algorithms. San Francisco, CA, Morgan Kaufmann (1991) 2–9.

    Google Scholar 

  32. Mühlenbein, H., Schlierkamp-Voosen, D.: The science of breeding and its application to the breeder genetic algorithm (BGA). Evolutionary Computation 1 (1993) 335–360.

    Article  Google Scholar 

  33. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Information characteristics and the structure of landscapes. Evolutionary Computation 8 (2000) 31–60.

    Article  Google Scholar 

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Vassilev, V.K., Miller, J.F. (2000). The Advantages of Landscape Neutrality in Digital Circuit Evolution. In: Miller, J., Thompson, A., Thomson, P., Fogarty, T.C. (eds) Evolvable Systems: From Biology to Hardware. ICES 2000. Lecture Notes in Computer Science, vol 1801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46406-9_25

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  • DOI: https://doi.org/10.1007/3-540-46406-9_25

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