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Genetic Programming Theory and Practice X pp 59–71Cite as

Introducing an Age-Varying Fitness Estimation Function

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Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

We present a method for estimating fitness functions that are computationally expensive for an exact evaluation. The proposed estimation method applies a number of partial evaluations based on incomplete information or uncertainties. We show how this method can yield results that are close to similar methods where fitness is measured over the entire dataset, but at a fraction of the speed or memory usage, and in a parallelizable manner. We describe our experience in applying this method to a real world application in the form of evolving equity trading strategies.

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Author information

Authors and Affiliations

  1. Genetic Finance LLC, 1 Market Street, San Francisco, CA, USA

    Babak Hodjat & Hormoz Shahrzad

Authors
  1. Babak Hodjat
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  2. Hormoz Shahrzad
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Corresponding author

Correspondence to Babak Hodjat .

Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Evolved Analytics Europe BVBA, Vossenlaan, Beerse, 2340, Belgium

    Ekaterina Vladislavleva

  3. , Department of Biochemistry and Molecular, The Pennsylvania State University, Wartik Laboratory, University Park, 16802, Pennsylvania, USA

    Marylyn D Ritchie

  4. , Institute for Quantitative, Dartmouth Medical School, Medical Center Drive, Lebanon, 03756, New Hampshire, USA

    Jason H. Moore

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Hodjat, B., Shahrzad, H. (2013). Introducing an Age-Varying Fitness Estimation Function. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6846-2_5

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  • DOI: https://doi.org/10.1007/978-1-4614-6846-2_5

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-6845-5

  • Online ISBN: 978-1-4614-6846-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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