From quasi-solutions to solution: An Evolutionary algorithm to solve CSP

  • María Cristina Riff Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1118)


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.


Constraint satisfaction Evolutionary algorithms Fitness evaluation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • María Cristina Riff Rojas
    • 1
  1. 1.INRIA, CERMICSSophia-AntipolisFrance

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