Discovering test statistics using genetic programming

Jason H. Moore, Yong Chen, Randal S. Olson, Moshe Sipper

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We describe a genetic programming-based system for the automated discovery of new test statistics. Specifically, our system was able to discover test statistics as powerful as the t-test for comparing sample means from two distributions with equal variances [1].

Original languageEnglish
Title of host publicationGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages29-30
Number of pages2
ISBN (Electronic)9781450367486
DOIs
StatePublished - 13 Jul 2019
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 13 Jul 201917 Jul 2019

Publication series

NameGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
Country/TerritoryCzech Republic
CityPrague
Period13/07/1917/07/19

Keywords

  • Genetic Programming
  • Optimization
  • Statistics
  • T-Test

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software

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