Applying Genetic Algorithms to Multiobjective Land Use Planning

This thesis explores the potential of applying genetic algorithms (GAs) to multi-objective land-use planning. Rural land managers, in the UK and elsewhere in Europe, are faced with an increasingly complex decision-making environment where a varied mix of goals have to be achieved. To address this problem, GA-based land-use planning tools have been developed that interface with a decision support system. The thesis rst evaluates the options for repre-senting the land-use planning problem within a GA framework. Two genotype represen-tations are proposed: a genotype mapping land-use directly to land parcels (Land-Block), and a representation making allocations indirectly via a greedy algorithm (P&P). The P&P representation requires novel breeding operators and post-evaluation processing, to identify and remove duplicate or defective genotypes. The performance of the two GAs ' was evaluated for a single-objective land-use plan-ning problem. Since both GAs found acceptable solutions, two multi-objective genetic algorithms (mGAs) were implemented based on the proposed representations. The goa