Multiobjective Land Use Optimisation Using Evolutionary Algorithms Jesper Bladt, University of Århus, Denmark Dept. of Systematic Botany EVAlife Group, dept. of Computer Science Supervisors: Anders Barfod, dept. of Systematic Botany, AAU Thiemo Krink, dept. of Computer Science, AAU Flemming Skov, NERI

Multiobjective Land Use Optimisation Conflicting land use interests, e.g. : Timber production Agriculture Tourism industry Conservation Management must find a compromise Complex problem Decision Support Systems and optimisation

Land Use Optimisation by EAs DATA GIS Conservation suitability Agricultural suitability Forestry suitability....various attributes SUITABILITY MAPS EVOLUTIONARY ALGORITHM OPTIMIZED SOLUTIONS

Evolutionary Algorithms (EAs)

Multiobjective Evolutionary algorithms More independent evaluations of a solution. Approaches - Weighted Sum Approach - Pareto Approach

Future Work Further development in the evaluation functions e.g. dynamic models, i nspiration from IBPM Further experimentation with operators and seeds