Skip to main content
Log in

Sufficient conditions for deceptive and easy binary functions

  • Published:
Annals of Mathematics and Artificial Intelligence Aims and scope Submit manuscript

Abstract

This paper finds sufficient conditions for fully or partially deceptive binary functions by calculating schema average fitness values. Deception conditions are first derived for functions of unitation (functions that depend only on the number of 1s in the string) and then extended for any binary function. The analysis is also extended to find a set of sufficient conditions for fully easy binary functions. It is found that the computational effort required to investigate full or partial deception in a problem of sizel using these sufficient conditions isO(2l) and using all necessary conditions of deception isO(4l). This calculation suggests that these sufficient conditions can be used to quickly test deception in a function. Furthermore, it is found that these conditions may also be systematically used to design a fully deceptive function by performing onlyO(l 2) comparisons and to design a partially deceptive function to orderk by performing onlyO(kl) comparisons. The analysis shows that in the class of functions of unitation satisfying these conditions of deception, an order-k partially deceptive function is also partially deceptive to any lower order. Finally, these sufficient conditions are used to investigate deception in a number of currently-used deceptive problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. D.H. Ackley,A Connectionist Machine for Genetic Hillclimbing (Kluwer, Boston, MA, 1987).

    Google Scholar 

  2. L. Davis, Bit-climbing, representational bias, and test suite design,Proc. 4th Int. Conf. on Genetic Algorithms (1991) pp. 18–23.

  3. K.A. De Jong, An analysis of the behavior of a class of genetic adaptive systems (Doctoral Dissertation, University of Michigan) Dissertation Abstracts Int. 36 (1975) 5140B.

    Google Scholar 

  4. K. Deb, Binary and floating-point function optimization using messy genetic algorithms (Doctoral Dissertation, University of Alabama and IlliGAL Report No. 91004), Dissertation Abstracts Int. 52 (1991) 2658B.

    Google Scholar 

  5. K. Deb and D.E. Goldberg, Analyzing deception in trap functions, IlliGAL Report No. 91009, Urbana: University of Illinois, Illinois Genetic Algorithms Laboratory (1991).

    Google Scholar 

  6. D.E. Goldberg, Simple genetic algorithms and the minimal deceptive problem, in:Genetic Algorithms and Simulated Annealing, ed. L. Davis (Pitman, London, 1987) pp. 74–88.

    Google Scholar 

  7. D.E. Goldberg, Genetic algorithms and Walsh functions: Part I, a gentle introduction, Complex Syst. 3 (1989) 129–152.

    Google Scholar 

  8. D.E. Goldberg, Genetic algorithms and Walsh functions: Part II, deception and its analysis, Complex Syst. 3 (1989) 153–171.

    Google Scholar 

  9. D.E. Goldberg, Construction of high-order deceptive functions using low-order Walsh coefficients, IlliGAL Report No. 90002, Urbana: University of Illinois, Illinois Genetic Algorithms Laboratory (1990).

    Google Scholar 

  10. D.E. Goldberg, K. Deb and J.H. Clark, Genetic algorithms, noise, and the sizing of populations, IlliGAL Report No. 91010, Urbana: University of Illinois Genetic Algorithms Laboratory (1991).

    Google Scholar 

  11. D.E. Goldberg, K. Deb and B. Korb, Messy genetic algorithms revisited: Studies in mixed size and scale, Complex Syst. 4 (1990) 415–444.

    Google Scholar 

  12. D.E. Goldberg, K. Deb and B. Korb, Don't worry, Be messy,Proc. 4th Int. Conf. on Genetic Algorithms and their Applications (1991) pp. 24–30.

  13. D.E. Goldberg, B. Korb and K. Deb, Messy genetic algorithms: Motivation, analysis, and first results, Complex Syst. 3 (1989) 493–530.

    Google Scholar 

  14. D.E. Goldberg and M. Rudnick, Genetic algorithms and the variance of fitness, Complex Syst. 5 (1991) 265–278.

    Google Scholar 

  15. J.J. Grefenstette, Deception considered harmful, Found. Genetic Algorithms (1993) 75–91.

  16. A. Homaifar, X. Qi and J. Fost, Analysis and design of a general GA deceptive problem,Proc. 4th Int. Conf. on Genetic Algorithms (1991) pp. 196–203.

  17. G.E. Liepins and M.D. Vose, Representational issues in genetic optimization, J. Exp. Theor. Artificial Intelligence 2(2) (1990) 4–30.

    Google Scholar 

  18. A.J. Mason, Partition coefficients, static deception and deceptive problems for non-binary alphabets,Proc. 4th Int. Conf. on Genetic Algorithms (1991) pp. 210–214.

  19. M.D. Vose and G.E. Liepins, Schema Disruption.Proc. 4th Int. Conf. on Genetic Algorithms (1991) pp. 237–242.

  20. D. Whitley, Fundamental principles of deception in genetic search, Found. Genetic Algorithms (1991) 221–241.

  21. S.W. Wilson, GA-easy does not imply steepest-ascent optimizable,Proc. 4th Int. Conf. on Genetic Algorithms (1991) pp. 85–89.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Deb, K., Goldberg, D.E. Sufficient conditions for deceptive and easy binary functions. Ann Math Artif Intell 10, 385–408 (1994). https://doi.org/10.1007/BF01531277

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01531277

Keywords

Navigation