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Counterexample-driven genetic programming without formal specifications

Published:08 July 2020Publication History

ABSTRACT

Counterexample-driven genetic programming (CDGP) uses specifications provided as formal constraints in order to generate the training cases used to evaluate the evolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the application of this method, called "informal CDGP," to software synthesis problems.

References

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    • Published in

      cover image ACM Conferences
      GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
      July 2020
      1982 pages
      ISBN:9781450371278
      DOI:10.1145/3377929

      Copyright © 2020 Owner/Author

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 8 July 2020

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