Adaptive selection of helper-objectives for test case generation

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

In this paper a method of adaptive selection of helper-objectives in evolutionary algorithms, which was previously applied to model problems only, is applied to generation of test cases for programming challenge tasks. The method is based on reinforcement learning. Experiments show that the proposed method performs equally well compared to the best helper-objectives selected by hand.
Original languageEnglish
Title of host publicationProceedings of the IEEE Congress on Evolutionary Computation
Subtitle of host publicationCEC 2013, Cancun, Mexico, June 20-23, 2013.
PublisherIEEE Press
Pages2245-2250
Number of pages6
ISBN (Print)9781479904532, 1479904538
DOIs
Publication statusPublished - 20 Jun 2013
Externally publishedYes
Event2013 IEEE Congress on Evolutionary Computation (CEC) - Cancun, Mexico
Duration: 20 Jun 201323 Jun 2013

Conference

Conference2013 IEEE Congress on Evolutionary Computation (CEC)
Country/TerritoryMexico
CityCancun
Period20 Jun 201323 Jun 2013

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