Abstract
Constraint Directed Network Artificial Immune System is an artificial immune algorithm, recently proposed, to solve constraint satisfaction problems. The algorithm has shown to be able to solve hard instances. However, some problems are still unsolved using this approach. In this paper, we propose a method to improve the search done by the algorithm. Our method can be included in other immune algorithms which manage constraints. The tests are carried out to solve very hard instances randomly generated of 3-colouring problems. The results show that using our method, the algorithm is able to solve more problems in less execution time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
de Castro, L.N., Timmis, J. (eds.): Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)
Cheeseman, P., Kanefsky, B., Taylor, W.: Where the really hard problems are. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI 1991), pp. 163–169 (1991)
Craenen, B.G.W., Eiben, A.E., van Hemert, J.I.: Comparing evolutionary algorithms on binary constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 7(5), 424–444 (2003)
Dasgupta, D. (ed.): Artificial Immune Systems and Their Applications. Springer, Heidelberg (2000)
Dozier, G., Bowen, J., Homaifar, A.: Solving constraint satisfaction problems using hybrid evolutionary search. IEEE Transactions on Evolutionary Computing 2(1), 23–33 (1998)
Eiben, A.E., van Hemert, J.I., Marchiori, E., Steenbeek, A.G.: Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 201–205. Springer, Heidelberg (1998)
Garrett, S.M.: Parameter-free, adaptive clonal selection. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2004), vol. 1, pp. 1052–1058 (2004)
Mackworth, A.K.: Consistency in network of relations. Artificial Intelligence 8, 99–118 (1977)
Marchiori, E.: Combining constraint processing and genetic algorithms for constraint satisfaction problems. In: Proceedings of the 7th International Conference on Genetic Algorithms (ICGA 1997), pp. 330–337 (1997)
Nannen, V., Eiben, A.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the Joint International Conference for Artificial Intelligence (IJCAI), pp. 975–980 (2007)
Riff, M.C.: A network-based adaptive evolutionary algorithm for csp. In: Metaheuristics: Advances and Trends in Local Search Paradigms for Optimisation, ch. 22, pp. 325–339. Kluwer Academic Publisher, Dordrecht (1998)
Riff, M.C., Zuniga, M.: Towards an immune system to solve csp. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapur, pp. 2837–2841 (2007)
Smith, B.M., Dyer, M.E.: Locating the phase transition in binary constraint satisfaction problems. Artificial Intelligence 81(1-2), 155–181 (1996)
Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 6(4), 347–357 (2002)
Tsang, E.P.K., Wang, C.J., Davenport, A., Voudouris, C., Lau, T.L.: A family of stochastic methods for constraint satisfaction and optimization. In: Proceedings of the First International Conference on The Practical Application of Constraint Technologies and Logic Programming (PACLP), London, pp. 359–383 (1999)
Minton, S.: Automatically Configuring Constraint Satisfaction Programs: A case study. Constraints 1(1) (1996)
Rossi, F., Petrie, C., Dhar, V.: On the equivalence of Constraint-Satisfaction Problems, Technical Report ACT-AI-222-89, MCC Corp., Austin, Texas (1989)
Freuder, E.: A sufficient condition of backtrack-free search. J. ACM 29, 24–32 (1982)
Freuder, E.: The Many Paths to Satisfaction. In: Meyer, M. (ed.) Constraint Processing. LNCS, vol. 923, pp. 103–119. Springer, Heidelberg (1995)
Haralick, Elliott: Increasing tree search efficiency for Constraint Satisfaction Problems. Artificial Intelligence 14, 263–313 (1980)
Kumar: Algorithms for constraint satisfaction problems: a survey. AI Magazine 13(1), 32–44 (1992)
Minton, J., Philips, L.: Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence 58, 161–205 (1992)
Minton, S.: Integrating heuristics for constraint satisfaction problems: A case study. In: Proceedings of the Eleventh National Conference on Artificial Intelligence (1993)
Brelaz: New methods to color vertices of a graph. Communications of the ACM 22, 251–256 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Riff, MC., Montero, E. (2009). Managing Diversity on an AIS That Solves 3-Colouring Problems. In: Andrews, P.S., et al. Artificial Immune Systems. ICARIS 2009. Lecture Notes in Computer Science, vol 5666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03246-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-03246-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03245-5
Online ISBN: 978-3-642-03246-2
eBook Packages: Computer ScienceComputer Science (R0)