Fuzzy reasoning and genetic algorithms for decision making problems in uncertain environment

In the present paper, genetic algorithms (GA) have been used to optimize the design of a generic expert system based on fuzzy logics. Using the classical branch-and-bound method, the latter performs a heuristic graph search to solve a decision-making problem under uncertain environment. After building this fuzzy expert system, we isolate a set of parameters which are important for the system efficiency, and we show how these parameters can be optimized “automatically” using Genetic Algorithms. This optimization brings significant improvement over the manual tuning of the parameters in the specific case of a fuzzy expert system developed for the automatic target recognition of armoured vehicles starting from short-range infra-red images