E Hajinezhad - Faculty of Mathematics, Iran University of Science and Technology, Narmak, Tehran, Iran
S Effati - Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
R Ghanbari - Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran.

In this paper, we introduce a novel arti cial neural network to solve the portfolio optimiza-tion problem. The proposed neural network is called the Mixed Tabu Machine since itsstructure is similar to the Tabu Machine, but includes both discrete and continues variables.Similar to the Hop eld network, the state of the Mixed Tabu Machine is updated to ndthe global minimum energy state. To escape from local minimum states of the energy in theMixed Tabu Machine, the state transition mechanism is controlled by a tabu search in bothdiscrete and continues search spaces. The experimental results for ve standard benchmarkdata sets show that the Mixed Tabu Machine can clearly obtain better solutions in less CPUtime than the recently proposed Hop eld network.

Tabu Machine, Hop eld network, portfolio optimization problem