HIZLI EVRİMSEL ENİYİLEME İÇİN YAPAY SİNİR AĞI KULLANILMASI.

In this paper, the Augmented Genetic Algorithm with Artificial Neural Network (AGANN) is expanded for optimization works, and its implementations to model problems are demonstrated. With the purpose of getting a faster algorithm, a neural network and a real coded genetic algorithm are hybridized in a new way. In this way, instead of predicting objective function calculation of a candidate, a properly trained neural network is used for predicting the candidate itself. At each step of the genetic process, using a simulated annealing based optimization procedure, the trained neural network produces an individual, which is a candidate solution of the optimization problem. Adding this candidate to the population at each step improves the exploration power of the genetic process. The proposed algorithm is tested for some test function problems. The results indicate that the computational efficiency of the implemented algorithm is tremendously high. Due to still being a genetic algorithm based technique, this method is also as robust as the pure genetic algorithms. [ABSTRACT FROM AUTHOR]