Neural networks approach for solving the Maximal Constraint Satisfaction Problems

In this paper, we propose a new approach to solve the maximal constraint satisfaction problems (Max-CSP) using the continuous Hopfield network. This approach is divided into two steps: the first step involves modeling the maximal constraint satisfaction problem as 0-1 quadratic programming subject to linear constraints (QP). The second step concerns applying the continuous Hopfield network (CHN) to solve the QP problem. Therefore, the generalized energy function associated to the CHN and an appropriate parameter-setting procedure about Max-CSP problems are given in detail. Finally, the proposed algorithm and some computational experiments solving the Max-CSP are shown.