2. 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

3. 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

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

5. Evolutionary Algorithms (EAs)

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

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