Abstract
This paper describes an Evolutionary Algorithm that repairs to solve Constraint Satisfaction Problems. Knowledge about properties of the constraints network can permit to define a fitness function which is used to improve the stochastic search. A selection mechanism which exploits this fitness function has been defined. The algorithm has been tested by running experiments on randomly generated 3-colouring graphs, with different constraints networks. We have also designed a specialized operator “permutation”, which permits to improve the performance of the classic crossover operator, reducing the generations number and a faster convergence to a global optimum, when the population is staying in a local optimum. The results suggest that the technique may be successfully applied to other CSP.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Bowen James, Gerry Dozier, Solving Constraint Satisfaction Problems Using A Genetic/Systematic Search Hybrid That Realizes When to Quit Proceedings of the Sixth International Conference on Genetic Algorithms pp. 122–129, 1995.
Cheeseman Peter, Bob Kanefsky, William Taylor, Where the Really Hard Problems Are Proc. of IJCAI-91, pp. 163–169, 1991
Dechter Rina, Enhancement schemes for constraint processing: backjumping, leaarning, and cutset decomposition. Artificial Intelligence 41, pp. 273–312, 1990.
Dozier Gerry, James Bowen, Dennis Bahler, Solving Small and Large Scale Constraint Satisfaction Problems Using a Heuristic-Based Microgenetic Algorithm Proc. of the First IEEE Conf on Evolutionary Computation, Orlando, pp 306–311, 1994.
Eiben A.E., P-E Raué, Zs. Ruttkay, Solving Constraint Satisfaction Problems Using Genetic Algorithms Proc. of the First IEEE Conf on Evolutionary Computation, Orlando, pp 542–547, 1994.
Eiben Ágoston, Paul-Erik Raué, Zsófia Ruttkay, GA-easy and GA-hard Constraint Satisfaction Problems Constraint Processing, Ed. Manfred Meyer, pp. 267–283, 1995.
Freuder Eugene. C, A sufficient condition of backtrack-free search J. ACM. 29, pp. 24–32, 1982.
Goldberg D.E., Genetic Algorithms in Search, Optimization and Machine Learning Ed. Addison-Wesley, 1989.
Michalewicz Zbigniew, Cezary Janikow, Handling Constraints in Genetic Algorithms Proc. of 4th Conference on GA, Morgan Kaufmann Publishers Los Altos, CA, pp 151–157, 1991.
Michalewicz Zbigniew, Genetic Algorithms + Data Structures=Evolution Programs Ed. Springer-Verlag, Artifial Intelligence Series, 1994.
Montanari U., Networks of constraints: Fundamental properties and applications to picture processing. Inf. Sci. 7, pp. 95–132, 1974.
Paredis Jan, Co-evolutionary Constraint Satisfaction Proceedings PPSN III, Int. Conference on Evolutionary Computation Israel, pp. 46–55, Oct. 1994.
Riff María-Cristina, Improving fitness function for CSP in an Evolutionary Algorithm. Rapport de Recherche CERMICS, Dec. 1995.
Thorton A.C., Genetic Algorithms versus Simulated Annealing: Satisfaction of Large Sets of Algebraic Mechanical Design Constraints Artifial Intelligence in Design, pp 381–398, 1994.
Tsang Edward, Applying Genetic Algorithms to Constraint Satisfaction Optimization Problems Proc. of ECAI-90, Pitman Publishing, pp 649–654, 1990
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rojas, M.C.R. (1996). From quasi-solutions to solution: An Evolutionary algorithm to solve CSP. In: Freuder, E.C. (eds) Principles and Practice of Constraint Programming — CP96. CP 1996. Lecture Notes in Computer Science, vol 1118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61551-2_87
Download citation
DOI: https://doi.org/10.1007/3-540-61551-2_87
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61551-4
Online ISBN: 978-3-540-70620-5
eBook Packages: Springer Book Archive