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Adaptive Walks with Noisy Fitness Measurements

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  • Bennett Levitan
  • Stuart Kauffman

Abstract

Adaptive walks are an optimization technique for searching a space of possible solutions, for example, a space of different molecules. The goal is to find a point in space (a molecule) optimal or near-optimal in some property, generally referred to as the ``fitness,'' such as its ability to bind to a given receptor. Adaptive walking is a powerful technique because of its ability to search many parts of the space in parallel. However, errors in the measurements will cause errors in the adaptive walks. Mutant molecules of higher fitness may be ignored or mutants of lower fitness may be accepted. To examine the effect of measurement error on adaptive walks, we simulate single-agent hill-climbing walks on NK landscapes of varying ruggedness where Gaussian noise is added to the fitness values to model measurement error. We consider both constant measurement noise and noise whose variance decays exponentially with fitness. We show that fitness-independent noise can cause walks to ``melt'' off the peaks in a landscape, wandering in larger regions as the noise increases. However, we also show that a small amount of noise actually helps the walk perform better than with no noise. For walks in which noise decreases exponentially with fitness, the most characteristic behavior is that the walk meanders throughout the landscape until it stumbles across a point of relatively high fitness, then it climbs the landscape toward the nearest peak. Finally, we characterize the balance between selection pressure and noise and show that there are several classes of walk dynamic behavior.

Suggested Citation

  • Bennett Levitan & Stuart Kauffman, 1995. "Adaptive Walks with Noisy Fitness Measurements," Working Papers 95-04-039, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:95-04-039
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    Cited by:

    1. Karén Hovhannisian & Marco Valente, 2005. "Modeling Directed Local Search Strategies on Technology," Computational Economics 0507001, University Library of Munich, Germany.
    2. Friederike Wall, 2017. "Learning To Incentivize In Different Modes Of Coordination," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-29, March.
    3. Friederike Wall, 2021. "Modeling Managerial Search Behavior based on Simon's Concept of Satisficing," Papers 2104.14002, arXiv.org, revised May 2021.
    4. Friederike Wall, 2019. "Emergence of Coordination in Growing Decision-Making Organizations: The Role of Complexity, Search Strategy, and Cost of Effort," Complexity, Hindawi, vol. 2019, pages 1-26, December.
    5. Bleda, Mercedes & Querbes, Adrien & Healey, Mark, 2021. "The influence of motivational factors on ongoing product design decisions," Journal of Business Research, Elsevier, vol. 129(C), pages 562-569.
    6. Friederike Wall, 2023. "Modeling managerial search behavior based on Simon’s concept of satisficing," Computational and Mathematical Organization Theory, Springer, vol. 29(2), pages 265-299, June.
    7. Karén Hovhannisian, 2004. "Imperfect Local Search Strategies on Technology Landscapes: Satisficing, Deliberate Experimentation and Memory Dependence," Computational Economics 0405009, University Library of Munich, Germany.
    8. Friederike Wall, 2015. "Beneficial Effects Of Randomized Organizational Change On Performance," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-23, August.
    9. Friederike Wall, 2016. "Agent-based modeling in managerial science: an illustrative survey and study," Review of Managerial Science, Springer, vol. 10(1), pages 135-193, January.
    10. Bennett Levitan, 1997. "Stochastic Modeling and Optimization of Phage Display," Working Papers 97-06-049, Santa Fe Institute.

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