Comparison of pulling back and penalty methods for constraints in DPMBGA

To solve real-world problems by genetic algorithms (GAs), GAs that have a strong searching capability are needed. In this paper, distributed probabilistic model building genetic algorithm (DPMBGA) is applied to solve the problems. The DPMBGA is an extended algorithm of probabilistic model building GA (PMBGA) and it also has a strong searching capability. In real world problems, constraints often exist. As such, mechanisms that can treat the constraints should be added to the GAs. Two mechanisms for treating constraints are the penalty method and pulling back method. The DPMBGA with penalty method and pulling back method is applied to truss structural optimization problems. Through a simulation, the searching capability and efficiency of the pulling back method and penalty method are discussed. From the discussion, it is concluded that the pulling back method can derive the solutions even if the problem is difficult. Compared to the penalty method, the number of individuals that violate the constraints is smaller in the pulling back method.