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Genetic Programming Theory and Practice XIII pp 169–183Cite as

Behavioral Program Synthesis: Insights and Prospects

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

Abstract

Genetic programming (GP) is a stochastic, iterative generate-and-test approach to synthesizing programs from tests, i.e. examples of the desired input-output mapping. The number of passed tests, or the total error in continuous domains, is a natural objective measure of a program’s performance and a common yardstick when experimentally comparing algorithms. In GP, it is also by default used to guide the evolutionary search process. An assumption that an objective function should also be an efficient ‘search driver’ is common for all metaheuristics, such as the evolutionary algorithms which GP is a member of. Programs are complex combinatorial structures that exhibit even more complex input-output behavior, and in this chapter we discuss why this complexity cannot be effectively reflected by a single scalar objective. In consequence, GP algorithms are systemically ‘underinformed’ about the characteristics of programs they operate on, and pay for this with unsatisfactory performance and limited scalability. This chapter advocates behavioral program synthesis, where programs are characterized by informative execution traces that enable multifaceted evaluation and substantially change the roles of components in an evolutionary infrastructure. We provide a unified perspective on past work in this area, discuss the consequences of the behavioral viewpoint, outlining the future avenues for program synthesis and the wider application areas that lie beyond.

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Acknowledgements

Krzysztof Krawiec acknowledges support from grant 09/91/DSPB/ 0572 and National Science Centre grant 2014/15/B/ST6/05205. Una-May O’Reilly acknowledges support from Li Ka Shing Foundation.

Author information

Authors and Affiliations

  1. Computational Intelligence Group, Institute of Computing Science, Poznan University of Technology, Poznań, Poland

    Krzysztof Krawiec

  2. Department of Computer Science, University of York, York, UK

    Jerry Swan

  3. York Centre for Complex Systems Analysis, University of York, York, UK

    Jerry Swan

  4. ALFA, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

    Una-May O’Reilly

Authors
  1. Krzysztof Krawiec
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  2. Jerry Swan
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  3. Una-May O’Reilly
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Corresponding author

Correspondence to Krzysztof Krawiec .

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Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

  2. Evolution Enterprises, Ann Arbor, Michigan, USA

    W.P. Worzel

  3. Evolved Analytics, Midland, Michigan, USA

    Mark Kotanchek

  4. Evolved Analytics LLC, Midland, Michigan, USA

    Arthur Kordon

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Krawiec, K., Swan, J., O’Reilly, UM. (2016). Behavioral Program Synthesis: Insights and Prospects. In: Riolo, R., Worzel, W., Kotanchek, M., Kordon, A. (eds) Genetic Programming Theory and Practice XIII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-34223-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-34223-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34221-4

  • Online ISBN: 978-3-319-34223-8

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