AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs

Lamia Sadeg-Belkacem, Zineb Habbas, Wassila Aggoune-Mtalaa

Abstract

Solving a Partial Constraint Satisfaction Problem consists in assigning values to all the variables of the problem such that a maximal subset of the constraints is satisfied. An efficient algorithm for large instances of such problems which are NP-hard does not exist yet. Decomposition methods enable to detect and exploit some crucial structures of the problems like the clusters, or the cuts, and then apply that knowledge to solve the problem. This knowledge can be explored by solving the different sub-problems separately before combining all the partial solutions in order to obtain a global one. This was the focus of a previous work which led to some generic algorithms based on decomposition and using an adaptive genetic algorithm, for solving the subproblems induced by the crucial structures coming from the decomposition. This paper aims to explore the decomposition differently. Indeed, here the knowledge is used to improve this adaptive genetic algorithm. A new adaptive genetic algorithm guided by structural knowledge is proposed. It is designed to be generic in order that any decomposition method can be used and different heuristics for the genetic operators are possible. To prove the effectiveness of this approach, three heuristics for the crossover step are investigated.

References

  1. Aardal, K., Van Hoessel, S., Koster, A., Mannino, C., and Sassano, A. (2007). Models and solution techniques for frequency assignment problems. Annals of Operations Research, 153:79-129.
  2. Allouche, D., Givry, S., and Schiex, T. (2010). Towards parallel non serial dynamic programming for solving hard weighted csp. In Proceedings of CSP' 2010, pages 53-60.
  3. Audhya, G. K., Sinha, K., Ghosh, S. C., and Sinha, B. P. (2011). A survey on the channel assignment problem in wireless networks. Wireless Communications and Mobile Computing, 11(5):583-609.
  4. CALMA-website (1995). Euclid Calma project. ftp://ftp.win.tue.nl/pub/techreports/CALMA/.
  5. Colombo, G. and Allen, S. M. (2007). Problem decomposition for minimum interference frequency assignment. In Proceedings of the IEEE Congress on Evolutionary Computation, Singapore.
  6. Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5).
  7. Fontaine, M., Loudni, S., and Boizumault, P. (2013). Exploiting tree decomposition for guiding neighborhoods exploration for vns. RAIRO Operations Research, 47(2):91-123.
  8. Girvan, M. and Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the USA, pages 7821-7826.
  9. Hale, W. K. (1980). Frequency assignment: Theory and applications. Proceedings IEEE, 68(12):1497-1514.
  10. Kolen, A. (2007). A genetic algorithm for the partial binary constraint satisfaction problem: an application to a frequency assignment problem. Statistica Neerlandica, 61(1):4-15.
  11. Koster, A., Van Hoessel, S., and Kolen, A. (2002). Solving partial constraint satisfaction problems with tree decomposition. Network Journal, 40(3):170-180.
  12. Lee, L. H. and Fan, Y. (2002). An adaptive real-coded genetic algorithm. Applied Artificial Intelligence, 16(6):457-486.
  13. Loudni, S., Fontaine, M., and Boizumault, P. (2012). Exploiting separators for guiding vns. Electronic Notes in Discrete Mathematics.
  14. Maniezzo, V. and Carbonaro, A. (2000). An ants heuristic for the frequency assignment problem. Computer and Information Science, 16:259-288.
  15. Metzger, B. H. (1970). Spectrum management technique. 38th National ORSA Meeting, Detroit.
  16. Newman, M. (2004). Fast algorithm for detecting community structure in networks. Physical Review, 69(6):066133.
  17. Ouali, A., Loudni, S., Loukil, L., Boizumault, P., and Lebbah, Y. (2014). Cooperative parallel decomposition guided vns for solving weighted csp. pages 100-114.
  18. Sadeg-Belkacem, L., Habbas, Z., Benbouzid-Sitayeb, F., and Singer, D. (2014). Decomposition techniques for solving frequency assignment problems (fap) a top-down approach. In International Conference on Agents and Artificial Intelligence (ICAART 2014), pages 477-484.
  19. Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1:27-64.
  20. Tate, D. M. and Smith, A. E. (1995). A genetic approach to the quadratic assignment problem. Computers & Operations Research, 22(1):73-83.
  21. Voudouris, C. and Tsang, E. (1995). Partial constraint satisfaction problems and guided local search. Technical report, Department of Computer Science, University of Essex. Technical Report CSM-25.
  22. Zhou, Z., Li, C.-M., Huang, C., and Xu, R. (2014). An exact algorithm with learning for the graph coloring problem. Computers & Operations Research, 51:282- 301.
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Paper Citation


in Harvard Style

Sadeg-Belkacem L., Habbas Z. and Aggoune-Mtalaa W. (2015). AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 78-89. DOI: 10.5220/0005196400780089


in Bibtex Style

@conference{icaart15,
author={Lamia Sadeg-Belkacem and Zineb Habbas and Wassila Aggoune-Mtalaa},
title={AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={78-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005196400780089},
isbn={978-989-758-074-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - AGAGD - An Adaptive Genetic Algorithm Guided by Decomposition for Solving PCSPs
SN - 978-989-758-074-1
AU - Sadeg-Belkacem L.
AU - Habbas Z.
AU - Aggoune-Mtalaa W.
PY - 2015
SP - 78
EP - 89
DO - 10.5220/0005196400780089