Automated discovery of test statistics using genetic programming

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

Research output: Contribution to journalLetterpeer-review

2 Scopus citations

Abstract

The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about the desirable properties of a good test statistic in addition to having an engine that can develop and explore candidate mathematical solutions with an intuitive representation. In this paper 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.

Original languageEnglish
Pages (from-to)127-137
Number of pages11
JournalGenetic Programming and Evolvable Machines
Volume20
Issue number1
DOIs
StatePublished - 1 Mar 2019

Keywords

  • Genetic programming
  • Optimization
  • Statistics
  • t test

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Science Applications

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