Exploring the Geographic Consequences of Public Policies Using Evolutionary Algorithms

摘要: Abstract Public policies with geographical consequences are often difficult to analyze because they affect multiple stakeholders competing objectives. While such problems fall conceptually into the domain of multiobjective evaluation, associated analytical techniques search for a single optimum solution. Within context problems, optimality means different things and, thus, an optimorum may not exist. In this article, we present new technique based on evolutionary algorithm (EA) that produces large number optimal and near-optimal solutions class land management problems. As implemented represent landscape patterns produce services meet stakeholder needs varying degrees. The construction curves illustrate trade-offs among various given limited resources is central approach. Decision makers can use these help find strike ...