Skip to main content

Research Repository

Advanced Search

Applying genetic algorithms to multi-objective land use planning.

Matthews, Keith B.; Craw, Susan; Elder, Stewart; Sibbald, Alan R.; MacKenzie, Iain

Authors

Keith B. Matthews

Stewart Elder

Alan R. Sibbald

Iain MacKenzie



Contributors

L. Darrell Whitley
Editor

David E. Goldberg
Editor

Erick Cantú-Paz
Editor

Lee Spector
Editor

Ian C. Parmee
Editor

Hans-Georg Beyer
Editor

Abstract

This paper explores the application of multi-objective genetic algorithms (mGAs) to rural land-use planning, a spatial allocation problem. Two mGAs are proposed. Both share an underlying structure of: fitness assignment using Pareto-dominance ranking, niche induction and an individual replacement strategy. They are differentiated by their representations: a fixed-length genotype composed of genes that map directly to a land parcel's use, and a variable-length, order-dependent representation making allocations indirectly via a greedy algorithm. The latter representation requires additional breeding operators to be defined and post-processing of the genotype structure, to identify and remove duplicate genotypes. The two mGAs are compared on a real land-use planning problem, and the strengths and weaknesses of the underlying framework - and of each representation - are identified.

Start Date Jul 10, 2000
Publication Date Jul 31, 2000
Publisher Morgan Kaufmann
Pages 613-620
ISBN 9781558607088
Institution Citation MATTHEWS, K.B., CRAW, S., ELDER, S., SIBBALD, A.R. and MACKENZIE, I. 2000. Applying genetic algorithms to multi-objective land use planning. In Whitley, L.D., Goldberg, D.E., CantĂș-Paz, E., Spector, L., Parmee, I.C. and Beyer, H.-G. (eds.) Proceedings of the 2000 Genetic and evolutionary computation conference (GECCO 2000): joint meeting of the 9th International conference on genetic algorithms (ICGA-2000), and the 5th Annual genetic programming conference (GP-2000), 10-12 July 2000, Las Vegas, USA. San Francisco: Morgan Kaufmann, pages 613-620.
Keywords Multi objective genetic algorithms; Land use planning

Files





You might also like



Downloadable Citations

;