Genetic algorithms: a fitness formulation for constrained minimization
Jonathan A. Wright, Raziyeh Farmani · 2001
A fitness formulation is presented for solving constrained optimization problems. In this method, the dimensionality of the problem is reduced by representing the constraint violations by a single infeasibility measure. The infeasibility measure is used to form a two stage penalty that is applied to the infeasible solutions. The performance of the method has been examined by its application to a set of eleven test cases. The results have been compared with previously published results from literature. It is shown that the method is able to find the optimum solutions. The proposed method is easy to implement and requires no parameters. The approach is also robust in its handling of both linear and nonlinear equality and inequality constraint functions. Furthermore, the method does not require an initial feasible solution.