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 generatio...Show MoreMetadata
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.
Published in: 2013 IEEE Congress on Evolutionary Computation
Date of Conference: 20-23 June 2013
Date Added to IEEE Xplore: 15 July 2013
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Test Case Generation ,
- Evolutionary Algorithms ,
- Limited Time ,
- Running Time ,
- Fitness Function ,
- Target Object ,
- Limited Memory ,
- Reward Function ,
- Reinforcement Learning Algorithm ,
- Optimization Goal ,
- Multi-objective Algorithm ,
- Evolution Operator ,
- Reinforcement Learning Agent ,
- Tournament Selection ,
- Successful Run
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Test Case Generation ,
- Evolutionary Algorithms ,
- Limited Time ,
- Running Time ,
- Fitness Function ,
- Target Object ,
- Limited Memory ,
- Reward Function ,
- Reinforcement Learning Algorithm ,
- Optimization Goal ,
- Multi-objective Algorithm ,
- Evolution Operator ,
- Reinforcement Learning Agent ,
- Tournament Selection ,
- Successful Run
