Discrete space location-allocation solutions from genetic algorithms

Genetic algorithms are adaptive sampling strategies based on information processing models from population genetics. Because they are able to sample a population broadly, they have the potential to out-perform existing heuristics for certain difficult classes of location problems. This paper describes reproductive plans in the context of adaptive optimization methods, and details the three genetic operators which are the core of the reproductive design. An algorithm is presented to illustrate applications to discrete-space location problems, particularly the p-median. The algorithm is unlikely to compete in terms of efficiency with existing p-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.