GIS-Based Evolutionary Approaches Using Multiple-Criteria Decision Analysis for Spatial Issues

Geographic information systems (GIS) have been considered as good decision support tools to provide the decision maker (DM). However, their spatial data functionalities fail to provide any report about the potentials of the information and cannot make rational choice between conflicting alternatives. Literature review shows that the integration of GIS with multiple-criteria decision analysis (MCDA) makes GIS more robust in decision making process. While MCDA are used to support DMs to deal and solve spatial multi-objective optimisation problems (SMOPs), the use of their methods are suited for eliciting the preferences of small group of stakeholders. Unlike to MCDA, Multi-Objective Evolutionary Algorithms (MOEA) perform well on solving SMOPS conflicting objectives since only one iteration of the algorithm gives rise to a set of trade-off solutions. However, only choosing better compromise doesn't completely solve the problem. Recently, a growing interest in combining MCDA and MOEA techniques has been seen. The chapter approaches the idea of integration of GIS, MOEA, and MCDA to solve SMOP.

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