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Improving the performance of genetic algorithms for land-use allocation problems

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Schwaab Jonas, Deb Kalyanmoy, Goodman Erik, Lautenbach Sven, van Strien Maarten J., Grêt-Regamey Adrienne,
Project Towards a more sustainable management of soil resources by redistribution of economic and ecological added and reduced values
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Original article (peer-reviewed)

Journal International Journal of Geographical Information Science
Page(s) 1 - 24
Title of proceedings International Journal of Geographical Information Science
DOI 10.1080/13658816.2017.1419249


Multi-objective optimization can be used to solve land-use allocation problems involving multiple conflicting objectives. In this paper, we show how genetic algorithms can be improved in order to effectively and efficiently solve multi-objective land-use allocation problems. Our focus lies on improving crossover and mutation operators of the genetic algorithms. We tested a range of different approaches either based on the literature or proposed for the first time. We applied them to a land-use allocation problem in Switzerland including two conflicting objectives: ensuring compact urban development and reducing the loss of agricultural productivity. We compared all approaches by calculating hypervolumes and by analysing the spread of the produced non-dominated fronts. Our results suggest that a combination of different mutation operators, of which at least one includes spatial heuristics, can help to find well-distributed fronts of non-dominated solutions. The tested modified crossover operators did not significantly improve the results. These findings provide a benchmark for multi-objective optimization of land-use allocation problems with promising prospectives for solving complex spatial planning problems.