Generation of tests for programming challenge tasks using multi-objective optimization

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


In this paper, an evolutionary approach to generation of test cases for programming challenge tasks is investigated. Multi-objective and single-objective evolutionary algorithms, as well as helper-objective selection strategies, are compared. Particularly, a previously proposed method of choosing between helper-objectives with reinforcement learning is considered. This method is applied to the multi-objective evolutionary algorithm for the first time. Results of the experiment show that the most reasonable approach for the considered problem is using multi-objective evolutionary algorithm with automated helper-objective selection.

Original languageEnglish
Title of host publicationGECCO '13 Companion
Subtitle of host publicationProceedings of the 15th annual conference companion on Genetic and evolutionary computation
EditorsChristian Blum
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Print)978-1-4503-1964-5
Publication statusPublished - 06 Jul 2013
Externally publishedYes
EventGECCO 2013 - Genetic and Evolutionary Computation Conference - Amsterdam, Netherlands
Duration: 06 Jul 201310 Jul 2013


ConferenceGECCO 2013 - Genetic and Evolutionary Computation Conference
Period06 Jul 201310 Jul 2013


  • genetic algorithms
  • helper-objectives
  • multi-objective
  • programming challenges
  • reinforcement learning
  • testing


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