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Neural estimation of interaction outcomes

Published: 02 July 2018 Publication History

Abstract

We propose Neural Estimation of Interaction Outcomes (NEIO), a method that reduces the number of required interactions between candidate solutions and tests in test-based problems. Given the outcomes of a random sample of all solution-test interactions, NEIO uses a neural network to predict the outcomes of remaining interactions and so estimate the fitness of programs. We apply NEIO to genetic programming (GP) problems, i.e. test-based problems in which candidate solutions are programs, while tests are examples of the desired input-output program behavior. In an empirical comparison to several reference methods on categorical GP benchmarks, NEIO attains the highest rank on the success rate of synthesizing correct programs.

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Cited By

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  • (2024)Accelerating Co-Evolutionary Learning Through Phylogeny-Informed Interaction EstimationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654250(427-430)Online publication date: 14-Jul-2024
  • (2020)Solving complex problems with coevolutionary algorithmsProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389874(832-858)Online publication date: 8-Jul-2020
  • (2019)Solving complex problems with coevolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323384(975-1001)Online publication date: 13-Jul-2019
  • Show More Cited By

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
July 2018
1578 pages
ISBN:9781450356183
DOI:10.1145/3205455
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 July 2018

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Author Tags

  1. autoencoders
  2. neural networks
  3. surrogate fitness
  4. test-based problems

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View all
  • (2024)Accelerating Co-Evolutionary Learning Through Phylogeny-Informed Interaction EstimationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654250(427-430)Online publication date: 14-Jul-2024
  • (2020)Solving complex problems with coevolutionary algorithmsProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389874(832-858)Online publication date: 8-Jul-2020
  • (2019)Solving complex problems with coevolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323384(975-1001)Online publication date: 13-Jul-2019
  • (2018)Solving complex problems with coevolutionary algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3207888(880-906)Online publication date: 6-Jul-2018

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