Merging fuzzy logic, neural networks and genetic computation in the design of a decision support system

The main goal of evolutionary computation is to provide a near optimal technique between exploration and exploitation of a search space. This approach is based on a genetic “engine” that operates the search of the optimal solution via biological-based assumptions. Selection of the optimal maintenance interventions activity, that can be tackled with success thanks to an evolutionary approach able to correct the distresses on the road pavement, is a very complex task. This paper presents an experimental architecture that improves the evolutionary aspect with additional benefits deriving from a synergistic combination of other powerful techniques, in particular neural networks and fuzzy logic. The best rules for managing pavement maintenance activities, developed through a genetic selection, are judged by a neural network. By an appropriate introduction of simple and efficient fuzzy identifiers, the features of the distress to treat can be described in an efficient and natural way. We describe the main advantages arising from this hybrid approach discussing the applicability of the method with experimental results.