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| Content Provider | ACM Digital Library |
|---|---|
| Author | Guizzo, Giovani Pozo, Aurora Trinidad Ramirez Fritsche, Gian Mauricio Vergilio, Silvia Regina |
| Abstract | Multi-objective evolutionary algorithms (MOEAs) have been efficiently applied to Search-Based Software Engineering (SBSE) problems. However, skilled software engineers waste significant effort designing such algorithms for a particular problem, adapting them, selecting operators and configuring parameters. Hyper-heuristics can help in these tasks by dynamically selecting or creating heuristics. Despite of such advantages, we observe a lack of works regarding this subject in the SBSE field. Considering this fact, this work introduces HITO, a Hyper-heuristic for the Integration and Test Order Problem. It includes a set of well-defined steps and is based on two selection functions (Choice Function and Multi-armed Bandit) to select the best low-level heuristic (combination of mutation and crossover operators) in each mating. To perform the selection, a quality measure is proposed to assess the performance of low-level heuristics throughout the evolutionary process. HITO was implemented using NSGA-II and evaluated to solve the integration and test order problem in seven systems. The introduced hyper-heuristic obtained the best results for all systems, when compared to a traditional algorithm. |
| Starting Page | 1343 |
| Ending Page | 1350 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450334723 |
| DOI | 10.1145/2739480.2754725 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-07-11 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Multi-objective algorithm Hyper-heuristic Search-based software engineering |
| Content Type | Text |
| Resource Type | Article |
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