  In this paper we present a new population based metaheuristic called the
fixed set search (FSS). The proposed approach represents a method of adding a
learning mechanism to the greedy randomized adaptive search procedure (GRASP).
The basic concept of FSS is to avoid focusing on specific high quality
solutions but on parts or elements that such solutions have. This is done
through fixing a set of elements that exist in such solutions and dedicating
computational effort to finding near optimal solutions for the underlying
subproblem. The simplicity of implementing the proposed method is illustrated
on the traveling salesman problem. Our computational experiments show that the
FSS manages to find significantly better solutions than the GRASP it is based
on and also the dynamic convexized method.
