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An adjustment model in a geometric constraint solving problem

An interesting problem related to geometric constraint solving is the choice of the "good" solution. The suitability and effectiveness of genetic algorithms applied to this problem has been demonstrated but their performance depends on the values assigned to their control parameters. Although there are recommendations in the specialised technical literature about values for these parameters, their optimal settings depend on the problem at hand. Therefore it would be interesting to define a model that automatically adjusts the values of the evolutive parameters as a function of the geometric problem.This paper proposes a meta-model that generates the recommendations for the right parameter values in genetic algorithms operating as a selector mechanism in constructive geometric constraint solvers. It should be stressed that the proposed model is general and automatic. This means that it is applicable to any context and works without the need for any user supervision.