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Cited by 195Evolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchEvolution and Genetic Dynamics

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Solving Uncompromising Problems With Lexicase Selection

Helmuth, Thomas, Spector, Lee, Matheson, James·2015·IEEE Transactions on Evolutionary Computation
DOI 10.1109/tevc.2014.2362729
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Abstract

We describe a broad class of problems, called “uncompromising problems,” which are characterized by the requirement that solutions must perform optimally on each of many test cases. Many of the problems that have long motivated genetic programming research, including the automation of many traditional programming tasks, are uncompromising. We describe and analyze the recently proposed “lexicase” parent selection algorithm and show that it can facilitate the solution of uncompromising problems by genetic programming. Unlike most traditional parent selection techniques, lexicase selection does not base selection on a fitness value that is aggregated over all test cases; rather, it considers test cases one at a time in random order. We present results comparing lexicase selection to more traditional parent selection methods, including standard tournament selection and implicit fitness sharing, on four uncompromising problems: 1) finding terms in finite algebras; 2) designing digital multipliers; 3) counting words in files; and 4) performing symbolic regression of the factorial function. We provide evidence that lexicase selection maintains higher levels of population diversity than other selection methods, which may partially explain its utility as a parent selection algorithm in the context of uncompromising problems.

Overview

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Crossref · 10.1109/tevc.2014.2362729

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Citations

Cites

Research works printed but not linked: 2

  1. luke, 2009, Essentials of Metaheuristics
  2. mckenzie, 1987, Algebras Lattices and Varieties, 1

Cited by

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  • Mathematical discoveries from program search with large language models

    Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature · doi:10.1038/s41586-023-06924-6 · Cited by 544

    “This approach, which aims to preserve diversity 55,56 , is related to Lexicase 57 in that both approaches consider a set of test cases for scoring an individual, and it is related to fitness uniform optimization 58 , which also clusters individuals on the basis of their fitness value; however, we…”
    Background

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Metadata

Authors
Helmuth, T., Spector, L., & Matheson, J.
Affiliations
  • University of Massachusetts Amherst (US)
  • Hampshire College (US)
ORCID
Venue
IEEE Transactions on Evolutionary Computation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-10
Volume
19
Issue
5
Pages
630-643
ISSN
  • 1089-778X (ISSN-L)
  • 1941-0026
DOI
10.1109/tevc.2014.2362729
OpenAlex
W2072473332
Cited by
195
References
33
Citations by year
2026: 13 · 2025: 23 · 2024: 23 · 2023: 16 · 2022: 17 · 2021: 14 · 2020: 14 · 2019: 16 · 2018: 24 · 2017: 14 · 2016: 17 · 2015: 3
FWCI
18.96
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Evolutionary Computation (publishedVersion)
Funders
National Science Foundation (1017817, 1129139, 1331283)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
lexicase selection, parent selection, genetic programming, tournament selection, population diversity, multi-objective optimization, symbolic regression, digital multiplier design, finite algebras, factorial function

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