Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study

This paper compares the performance of genetic algorithms (GAs) on large-scale maximum expected coverage problems to other heuristic approaches. We focus our attention on a particular formulation with a nonlinear objective function to be optimized over a convex set. The solutions obtained by the best genetic algorithm are compared to Daskin's heuristic and the optimal or best solutions obtained by solving the corresponding integer linear programming problems. We show that at least one of the GAs yields optimal or near-optimal solutions in a reasonable amount of time.