A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem - Guizzo, Giovani; Vergilio, Silvia R.; Pozo, Aurora T.R.; Fritsche, Gian M. (Applied soft computing, 2017)
[Display omitted] •This paper presents HITO, a hyper-heuristic to solve the Integration and Test Order Problem.•HITO adaptively selects search operators used by Multi-objective and Evolutionary Algorithms.•HITO includes a novel performance assessment metric based on the Pareto dominance concept.•HITO implements two selection methods: Choice Function and Multi-Armed Bandit.•The experimental results show that HITO outperformed its random version, the traditional NSGA-II, and the state of the art MOEA/DD. The field of Search-Based Software Engineering (SBSE) has widely utilized Multi-Objective Evolutionary Algorithms (MOEAs) to solve complex software engineering problems. However, the use of such algorithms can be a hard task for the software engineer, mainly due to the significant range of parameter and algorithm choices. To help in this task, the use of Hyper-heuristics is recommended. Hyper-heuristics can select or generate low-level heuristics while optimization algorithms are executed, and thus can be generically applied. Despite their benefits, we find only a few works using hyper-heuristics in the SBSE field. Considering this fact, we describe HITO, a Hyper-heuristic for the Integration and Test Order Problem, to adaptively select search operators while MOEAs are executed using one of the selection methods: Choice Function and Multi-Armed Bandit. The experimental results show that HITO can outperform the traditional MOEAs NSGA-II and MOEA/DD. HITO is also a generic algorithm, since the user does not need to select crossover and mutation operators, nor adjust their parameters.