- author = "Thaina Mariani and Giovani Guizzo and Silvia Regina Vergilio and Aurora Trinidad Ramirez Pozo",
- title = "Grammatical Evolution for the Multi-Objective Integration and Test Order Problem",
- booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation",
- year = "2016",
- editor = "Tobias Friedrich",
- pages = "1069--1076",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, SBSE, search based software engineering, multi-objective, hyper-heuristic, evolutionary algorithm",
- month = "20-24 " # jul,
- organisation = "SIGEVO",
- address = "Denver, USA",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- isbn13 = "978-1-4503-4206-3",
-
DOI = "
doi:10.1145/2908812.2908816",
- abstract = "Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms.",
-
notes = "Federal University of Parana
GECCO-2016 A Recombination of the 25th International Conference on Genetic Algorithms (ICGA-2016) and the 21st Annual Genetic Programming Conference (GP-2016)",
