Optimization of tourism impacts within protected areas by means of genetic algorithms

The search for a balance between nature conservation and tourism development within protected areas is becoming an increasingly multifaceted problem worldwide, as outlined by an increasing number of authors and highlighted at several international events. Since it is unlikely that all management objectives will reach their optimum values simultaneously, an optimization approach is required to meet multiple, conflicting goals and to obtain an overall trade-off in terms of the conceived objectives.In this paper we propose a new model for optimizing the allocation of tourist infrastructures (refuges and camping sites), and apply it to a protected area in the European Alps, where tourism has grown considerably in recent years. To reach this goal, a complex model based on genetic algorithms was required (instead of a common multicriteria analysis) to obtain a complex interplay in the form of a dynamical simulation where candidate solutions are interactively evaluated. In accordance with local actors, we selected 18 quantifiable criteria encompassing all relevant tourist activities within the study area. These were translated into geographic information system (GIS) layers, submitted to a genetic optimization procedure and compared the performances of the optimized tourist allocations with those of existing infrastructures and with worst case scenarios. Resulting tourist allocations perform very well, while existing tourist sites behaved halfway between the fittest and the least fit genetic solutions, since they prioritized logistic and safety criteria rather than ecological ones.The proposed model is a very flexible and effective tool, easily exportable to any protected area, with implications for researchers and policymakers who aim to provide an effective balance between nature and human impact.