Learning from evolved next release problem ...
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
Learning from evolved next release problem instances
Auteur(s) :
Ren, Zhilei [Auteur]
Dalian University of Technology
Jiang, He [Auteur]
Dalian University of Technology
Xuan, Jifeng [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Zhang, Shuwei [Auteur]
Dalian University of Technology
Luo, Zhongxuan [Auteur]
Dalian University of Technology
Dalian University of Technology
Jiang, He [Auteur]
Dalian University of Technology
Xuan, Jifeng [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Zhang, Shuwei [Auteur]
Dalian University of Technology
Luo, Zhongxuan [Auteur]
Dalian University of Technology
Titre de la manifestation scientifique :
GECCO - Genetic and Evolutionary Computation Conference, 2014
Organisateur(s) de la manifestation scientifique :
ACM SIGEVO
Ville :
Vancouver, BC
Pays :
Canada
Date de début de la manifestation scientifique :
2014-07-12
Date de publication :
2014
Mot(s)-clé(s) en anglais :
Genetic algorithm
Next release problem
Problem hardness
Next release problem
Problem hardness
Discipline(s) HAL :
Informatique [cs]/Algorithme et structure de données [cs.DS]
Résumé en anglais : [en]
Taking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances ...
Lire la suite >Taking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are either hard or easy for the target heuristic (GRASP in this study), to investigate where a heuristic works well and where it does not, when facing a software engineering problem. Thereafter, we use a feature-based approach to predict the hardness of the evolved instances, with respect to the target heuristic. Experimental results reveal that, the proposed algorithm is able to evolve NRP instances with different hardness. Furthermore, the problem-specific features enables the prediction of the target heuristic's performance.Lire moins >
Lire la suite >Taking the Next Release Problem (NRP) as a case study, we intend to analyze the relationship between heuristics and the software engineering problem instances. We adopt an evolutionary algorithm to evolve NRP instances that are either hard or easy for the target heuristic (GRASP in this study), to investigate where a heuristic works well and where it does not, when facing a software engineering problem. Thereafter, we use a feature-based approach to predict the hardness of the evolved instances, with respect to the target heuristic. Experimental results reveal that, the proposed algorithm is able to evolve NRP instances with different hardness. Furthermore, the problem-specific features enables the prediction of the target heuristic's performance.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.inria.fr/hal-01087436/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/hal-01087436/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- pap299-he.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- pap299-he.pdf
- Accès libre
- Accéder au document