Paper Info
Title
Adaptive selection of helper-objectives for test case generation
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.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557836
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
evolutionary computation,learning (artificial intelligence),adaptive helper-objectives selection,evolutionary algorithms,reinforcement learning,test case generation
Computer science,Learnable Evolution Model,Evolutionary computation,Genetic programming,Test case,Artificial intelligence,Evolutionary programming,Genetic algorithm,Machine learning,Reinforcement learning,Learning classifier system
Conference
ISBN
Citations 
PageRank 
978-1-4799-0452-5
5
0.53
References 
Authors
15
2
Name
Order
Citations
PageRank
Maxim Buzdalov114125.29
Arina Buzdalova2619.42