In this study, we propose multi-stage and hybrid real-coded genetic algorithm. In the proposed algorithm, there are two stages. In the first stage, Real-coded Genetic Algorithm with Active Constraints (RGAAC) is applied to find a solution that is close to the global optimum. In RGAAC, individuals who are out of the feasible region are pulled back into feasible region. Therefore, the effective s...

Multilayer perceptrons (MLPs) are widely used for pattern classification and regression problems. Backpropagation (BP) algorithm is known technique in the training of multilayer perceptrons. However for its optimum training convergence, the learning and momentum parameters need to be tuned on trial and error method. Further, sometimes the backpropagation algorithm fails to achieve global conver...

the capacitated vehicle routing problem (cvrp) is a well-known combinatorial optimization problem that holds a central place in logistics management. the vehicle routing is an applied task in the industrial transportation for which an optimal solution will lead us to better services, save more time and ultimately increase in customer satisfaction. this problem is classified into np-hard problem...

the paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. some mathematical vector models were obtained for control system multicriterion quality indices with due ...

logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted the attention of many researchers. transportation network design is then one of the most important fields of logistic network. this study is concerned with designing a multi-stage and multi-product logistic network. at first, a mixed integer nonlinear programming model...

neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

  Improving knowledge worker productivity has been one of the most important tasks of the century. However, we have few measures or management interventions to make such improvement possible, and it is difficult to identify patterns that should be followed by knowledge workers because systems and processes in an organization are often regarded as a death blow to creativity. In this paper, we se...