Using constraint satisfaction in genetic algorithms

Existing methods to handle constraints in genetic algorithms (GA) are often computationally expensive or problem domain specific. In this paper, an approach to handle constraints in GA with the use of constraint satisfaction principles is proposed to overcome those drawbacks. Each chromosome representing a set of constrained variables in GA is interpreted as an instance of the same constraint satisfaction problem represented by a constraint network. Dynamic constraint consistency checking and constraint propagation is performed during the main GA simulation process. Unfeasible solutions are detected and eliminated from the search space at early stages of GA simulation process without requiring the problem specific representation or generation operators to provide feasible solutions. Constraint satisfaction is applied actively in GA during initialization, crossover and mutation operations to advantage.