Generation of Tests for Programming Challenge Tasks Using Helper-Objectives
Generation of performance tests for programming challenge tasks is considered. A number of evolutionary approaches are compared on two different solutions of an example problem. It is shown that using helper-objectives enhances evolutionary algorithms in the considered case. The general approach involves automated selection of such objectives.
Keywordstest generation programming challenges multi-objective evolutionary algorithms multi-objectivization helper-objectives
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- 1.ACM International Collegiate Programming Contest, http://cm.baylor.edu/welcome.icpc
- 2.Timus Online Judge. The Problem Archive with Online Judge System, http://acm.timus.ru
- 3.Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer (2003)Google Scholar
- 7.Buzdalova, A., Buzdalov, M.: Increasing Efficiency of Evolutionary Algorithms by Choosing between Auxiliary Fitness Functions with Reinforcement Learning. In: 11th International Conference on Machine Learning and Applications, pp. 150–155. IEEE (2012)Google Scholar
- 8.Pisinger, D.: Algorithms for Knapsack Problems. PhD Thesis, University of Copenhagen (1995)Google Scholar
- 10.D’Souza, Rio G. L., Chandra Sekaran, K., Kandasamy, A.: Improved NSGA-II Based on a Novel Ranking Scheme. Computing Research Repository. ID: abs/1002.4005 (2010)Google Scholar
- 11.Strehl, A.L., Li, L., Wiewora, E., Langford, J., Littman, M.L.: PAC model-free reinforcement learning. In: 23rd International Conference on Machine Learning, pp. 881–888 (2006)Google Scholar