Abstract
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 thep-median. The algorithm is unlikely to compete in terms of efficiency with existingp-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
A.D. Bethke,Genetic Algorithms as Function Optimizers (Logic of Computers Group, Dept. of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1981).
E. Bonomi and J.-L. Lutton, TheN-city travelling salesman problem; Statistical mechanics and the metropolis algorithm, Ecole Polytechnique Fédérale de Lausanne, Département de Mathématique, Ecublens, Lausanne (1983).
G.C. Brockus, Shortest path optimization using a genetics search technique, in:Modeling and Simulation 14, Proc. 14th Annual Pittsburgh Conf. (1983) p. 241.
K. DeJong, in:Synopsis: An Interdisciplinary Workshop in Adaptive Systems, ed. J.R. Sampson (Logic of Computers Group, Dept. of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1981) p. 26.
J.H. Holland,Adaption in Natural and Artificial Systems (The University of Michigan Press, Ann Arbor, 1975).
P. Kainen, in:Synopsis: An Interdisciplinary Workshop in Adaptive Systems ed. J.R. Sampson (Logic of Computers Group, Dept. of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1981) p. 75.
S. Kirkpatrick, C.D. Gelatt, Jr. and M.P. Vecchi, Optimization by simulated annealing, Science 220, 4598(1983)671.
C.C. Skiscim and B.L. Golden, Optimization by simulated annealing: A preliminary computational study for the TSP. Paper presented at the NIHE Summer School on Combinatorial Optimization, Dublin, Ireland (1983).
A. Wetzel, in:Synopsis: An Interdisciplinary Workshop in Adaptive Systems ed. J.R. Sampson (Logic of Computers Group, Dept. of Computer and Communication Sciences, University of Michigan, Ann Arbor, 1981) p. 85.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Hosage, C.M., Goodchild, M.F. Discrete space location-allocation solutions from genetic algorithms. Ann Oper Res 6, 35–46 (1986). https://doi.org/10.1007/BF02027381
Issue Date:
DOI: https://doi.org/10.1007/BF02027381