A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

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A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

Guizzo, G; Vergilio, SR; Pozo, ATR; Fritsche, GM; (2017) A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem. Applied Soft Computing , 56 pp. 331-344. 10.1016/j.asoc.2017.03.012. Green open access

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Abstract

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.

Type:	Article
Title:	A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1016/j.asoc.2017.03.012
Publisher version:	https://doi.org/10.1016/j.asoc.2017.03.012
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Metaheuristic, Hyper-heuristic, Multi-objective algorithm, Search-Based Software Engineering, Software testing
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10076010