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Particularity

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Genetic Programming Theory and Practice XX

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

Abstract

We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this “particularity” approach from the use of lexicase selection in genetic programming to “particularist” approaches to other forms of machine learning and to the design of adaptive systems more generally.

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Notes

  1. 1.

    This lexicographic processing of fitness cases is the reason that lexicase selection is so named.

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Acknowledgements

We thank Bill Tozier, Anil Saini, Eddie Pantridge, Andrew Ni, Nic McPheee, Tom Helmuth, Ramita Dhamrongsirivadh, and other members of the Amherst College PUSH lab for stimulating conversations that helped us to develop the ideas described in this paper. We also thank participants in the 2023 Genetic Programming Theory and Practice workshop, and particularly Alex Lalejini, Erik Hemberg, and Joel Lehman, who commented on a draft. This material is based upon work supported by the National Science Foundation under Grant No. 2117377. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work was also performed in part using high-performance computing equipment obtained under a grant from the Collaborative R &D Fund managed by the Massachusetts Technology Collaborative.

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Authors and Affiliations

  1. Amherst College, Amherst, MA, 01002, USA

    Lee Spector

  2. University of Massachusetts, Amherst, Amherst, MA, 01002, USA

    Lee Spector, Li Ding & Ryan Boldi

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  1. Lee Spector
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  2. Li Ding
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  3. Ryan Boldi
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Correspondence to Lee Spector .

Editor information

Editors and Affiliations

  1. School of Information, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan Winkler

  2. Engineering Sciences Graduate Program, Tecnológico Nacional de México, IT de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Charles Ofria

  4. School of Computing, Queen's University, Kingston, ON, Canada

    Ting Hu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Spector, L., Ding, L., Boldi, R. (2024). Particularity. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_9

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  • DOI: https://doi.org/10.1007/978-981-99-8413-8_9

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  • Online ISBN: 978-981-99-8413-8

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