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Fluctuating crosstalk, deterministic noise, and GA scalability

Published: 08 July 2006 Publication History
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    This paper extends previous work showing how fluctuating crosstalk in a deterministic fitness function introduces noise into genetic algorithms. In that work, we modeled fluctuating crosstalk or nonlinear interactions among building blocks via higher-order Walsh coefficients. The fluctuating crosstalk behaved like exogenous noise and could be handled by increasing the population size and run duration. This behavior held until the strength of the crosstalk far exceeded the underlying fitness variance by a certain factor empirically observed. This paper extends that work by considering fluctuating crosstalk effects on genetic algorithm scalability using smaller-ordered Walsh coefficients on two extremes of building block scaling: uniformly-scaled and exponentially-scaled building blocks. Uniformly-scaled building blocks prove to be more sensitive to fluctuating crosstalk than do exponentially-scaled building blocks in terms of function evaluations and run duration but less sensitive to population sizing for large building-block interactions. Our results also have implications for the relative performance of building-block-wise mutation over crossover.

    References

    [1]
    Sastry, K., Goldberg, D.E.: Let's get ready to rumble: Crossover versus mutation head to head. Proceedings of the 2004 Genetic and Evolutionary Computation Conference 2 (2004) 126--137 Also IlliGAL Report No. 2004005.
    [2]
    Goldberg, D.E.: Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Acadamic Publishers, Boston, MA (2002)
    [3]
    Sastry, K., Winward, P., Goldberg, D.E., Lima, C.: Fluctuating crosstalk as a source of deterministic noise and its effects on GA scalability. Applications of Evolutionary Computing, EvoWorkshops (2006) 740--751
    [4]
    Davidor, Y.: Epistasis Variance: A Viewpoint on GA-hardness. foga91 (1991) 23--35
    [5]
    Naudts, B., Kallel, L.: Some Facts about so-called GA-hardness Measures. Tech. Rep. No. 379, Ecole Polytechnique, CMAP, France (1998)
    [6]
    Heckendorn, R.B., Whitley, D.: Predicting Epistasis from Mathematical Models. Evolutionary Computation 7(1) (1999) 69--101
    [7]
    Mühlenbein, H., Mahnig, T., Rodriguez, A.O.: Schemata, Distributions and Graphical Models in Evolutionary Optimization. Journal of Heuristics 5 (1999) 215--247
    [8]
    Pelikan, M., Goldberg, D.E., Lobo, F.G.: A Survey of Optimization by Building and Using Probabilistic Models. Comput. Optim. Appl. 21(1) (2002) 5--20
    [9]
    Lauritzen, S.L.: Graphical Models. Oxford University Press (1998)
    [10]
    Beasley, D., Bull, D.R., Martin, R.R.: Reducing Epistasis in Combinatorial Problems by Expansive Coding. In: ICGA. (1993) 400--407
    [11]
    Barbulescu, L., Watson, J.P., Whitley, L.D.: Dynamic Representations and Escaping Local Optima: Improving Genetic Algorithms and Local Search. In: AAAI/IAAI. (2000) 879--884
    [12]
    Pelikan, M., Lobo, F., Goldberg, D.E.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21 (2002) 5--20 (Also IlliGAL Report No. 99018).
    [13]
    Bethke, A.D.: Genetic Algorithms as Function Optimizers. PhD thesis, The University of Michigan (1981)
    [14]
    Goldberg, D.E.: Genetic Algorithms and Walsh Functions: Part I, a Gentle Introduction. Complex Systems 3(2) (1989) 129--152 (Also TCGA Report 88006).
    [15]
    Sastry, K.: Evaluation-Relaxation Schemes for Genetic and Evolutionary Algorithms. Master's thesis, University of Illinois at Urbana-Champaign, General Engineering Department, Urbana, IL (2001) (Also IlliGAL Report No. 2002004).
    [16]
    Harik, G., Cantú-Paz, E., Goldberg, D.E., Miller, B.L.: The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations. Evolutionary Computation 7(3) (1999) 231--253 (Also IlliGAL Report No. 96004).
    [17]
    Miller, B.L., Goldberg, D.E.: Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise. Evolutionary Computation 4(2) (1996) 113--131 (Also IlliGAL Report No. 95009).
    [18]
    Rudnick, M.: Genetic Algorithms and Fitness Variance with an Application to the Automated Design of Artificial Neural Networks. PhD thesis, Oregon Graduate Institute of Science and Technology, Portland (1992)
    [19]
    Goldberg, D.E., Segrest, P.: Finite Markov chain analysis of genetic algorithms. Proceedings of the Second International Conference on Genetic Algorithms (1987) 1--8

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    • (2012)Modeling and replicating higher-order dependencies in genetic algorithms2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256648(1-8)Online publication date: Jun-2012

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    cover image ACM Conferences
    GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation
    July 2006
    2004 pages
    ISBN:1595931864
    DOI:10.1145/1143997
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    Published: 08 July 2006

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    Author Tags

    1. deterministic noise
    2. fluctuating crosstalk
    3. genetic algorithm
    4. problem difficulty
    5. scalability

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    GECCO06: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2006
    Washington, Seattle, USA

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    GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2012)Modeling and replicating higher-order dependencies in genetic algorithms2012 IEEE Congress on Evolutionary Computation10.1109/CEC.2012.6256648(1-8)Online publication date: Jun-2012

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