The RandomCsp library has three goals

To facilitate as a suit of programs to create and analyse randomly created binary constraint satisfaction problems,

for those who do not like to work with libraries for any reason a suite of programs is included that provides a simple interface to the library. With these programs it is easy to create a set of random problem instances, to verify solutions and to analyse instances on a number of features.

 

To be used as a library to implement and test new or existing constraint satisfaction solving techniques,

the library part has an extended documentation of its class hierarchy that helps new developers on their way creating new tools or solving techniques for binary constraint satisfaction. At the same time the library allows testing using a number of theoretical models.

 

To be freely available for anyone,

the library and programs that come with it are all licensed under the Gnu Public License, ensuring free use forever.

Changes with previous (1.7.0) version: Added Model RB by Xu and Li; works now with GCC-3.3.2; changed default output from list to matrix

Changes with previous (1.6.1) version: Added Model F by MacIntyre etal.: an improvement over Model E by Achlioptas et al. when concerned with the density property in constraint satisfaction problems.

The publications below all use RandomCsp

An Experimental Comparison of SAWing EAs for a new Class of Random Binary CSPs

B.G.W. Craenen & A.E. Eiben

In Proceedings of 2002 Congress on Evolutionary Computation (CEC'02), pages 878-883. Honolulu, Hawaii, 12-17 May 2002. IEEE Computer Society Press.

 

Comparing classical methods for solving binary constraint satisfaction problems with state of the art evolutionary computation

J.I. van Hemert

In Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, and Günther Raidl, editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim, volume 2279 of LNCS, pages 81-90, Kinsale, Ireland, 3-4 April 2002. Springer-Verlag.

 

Stepwise Adaption of Weights with Decay on Constraint Satisfaction Problems

B.G.W. Craenen & A.E. Eiben

Genetic and Evolutionary Computation Conference (GECCO-2001); July 2001.

 

A Genetic Local Search Algorithm for Random Binary Constraint Satisfaction Problems

E. Marchiori & A. Steenbeek

Proceedings of the 14th Annual Symposium on Applied Computing, (SAC 2000), pp. 458-462, 2000.

 

Solving Constraint Satisfaction Problems with Heuristic-based Evolutionary Algorithms

B.G.W. Craenen & A.E. Eiben & E. Marchiori

Congress on Evolutionary Computation, (CEC-2000), 2000.

 

Combining Local Search and Fitness Function Adaptation in a GA for Solving Binary Constraint Satisfaction Problems

B.G.W. Craenen & A.E. Eiben & E. Marchiori & A. Steenbeek

Genetic and Evolutionary Computation Conference (GECCO-2000); July 2000.

 

Constraint satisfaction problems and evolutionary algorithms: A reality check

J.I. van Hemert

In A. van den Bosch and H. Weigand, editors, Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00), pages 267-274, De Efteling, Tilburg, The Netherlands, 1-2 November 2000.

 

An Experimental Comparison of Three Different Heuristic Genetic Algorithms for Solving Constraint Satisfaction Problems

B.G.W. Craenen

Master's thesis, Leiden University, 1998.

 

Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function

A.E. Eiben & J.I. van Hemert & E. Marchiori & A. Steenbeek

In A.E. Eiben, Th. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Proceedings of the 5th Conference on Parallel Problem Solving from Nature, number 1498 in lncs, pages 196-205, Berlin, 1998. Springer.

 

Applying adaptive evolutionary algorithms to hard problems

J.I. van Hemert

Master's thesis, Leiden University, 1998.