Mixed Tabu machine for portfolio optimization problem

In this paper, we introduce a novel artificial neural network to solve the portfolio optimization problem. The proposed neural network is called the Mixed Tabu Machine since its structure is similar to the Tabu Machine, but includes both discrete and continues variables. Similar to the Hopfield network, the state of the Mixed Tabu Machine is updated to find the global minimum energy state. To escape from local minimum states of the energy in the Mixed Tabu Machine, the state transition mechanism is controlled by a tabu search in both discrete and continues search spaces. The experimental results for five standard benchmark data sets show that the Mixed Tabu Machine can clearly obtain better solutions in less CPU time than the recently proposed Hopfield network.