Towards a more efficient stochastic constraint solver
E-GENET shows certain success on extending GENET for non-binary CSP's. However, the generic constraint representation scheme of E-GENET induces the problem of storing too many penalty values in constraint nodes and the min-conflicts heuristic is not efficient enough on some problems. To overcome these two weaknesses and further improve the performance, we propose several modifications. All of them together can boost the efficiency of E-GENET without resorting to modifying the underlying network model or the convergence procedure in an ad hoc manner. The performance of modified E-GENET also compares well against that of CHIP.
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- 1.N. Beldiceanu and E. Contejean. Introducing global constraints in CHIP. Journal of Mathematical and Computer Modelling, 17(7):57–73, 1994.Google Scholar
- 2.A. Davenport, E. Tsang, C. J. Wang, and K. Zhu. GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement. In Proc. 12th National Conference on Artificial Intelligence, 1994.Google Scholar
- 3.D. Diaz and P. Codognet. A minimal extension of the WAM for clp(FD). In Proc. 10th International Conference on Logic Programming, pages 774–790, 1993.Google Scholar
- 4.M. Dincbas, H. Simonis, and P. Van Hentenryck. Solving car sequencing problem in contraint logic programming. In Proc. European Conference on AI, pages 290–295, 1988.Google Scholar
- 5.M. Dincbas, P. Van Hentenryck, H. Simonis, A. Aggoun, T. Graf, and F. Berthier. The constraint logic programming language CHIP. In Proc. International Conference on Fifth Generation Computer Systems, pages 693–702, December 1988.Google Scholar
- 6.E. C. Freuder. Partial constraint satisfaction. In Proc. 11th International Joint Conference on AI, pages 278–283, 1989.Google Scholar
- 7.J.H.M. Lee, H.F. Leung, and H.W. Won. Extending GENET for non-binary CSP's. In Proc. 7th International Conference on Tools with Artificial Intelligence, pages 338–343, 1995.Google Scholar
- 8.S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58:161–205, 1992.Google Scholar
- 9.The COSYTEC Team. CHIP V4.1 User Manuals, 1994.Google Scholar
- 10.P. Van Hentenryck. Constraint Satisfaction in Logic Programming. The MIT Press, 1989.Google Scholar
- 11.T. Warwick and E.P.K. Tsang. Tackling car sequencing problems using a generic genetic algorithm. Evolutionary Computation, 3(3):267–298, 1995. (to appear).Google Scholar