Fuzzy-neural tuned genetic algorithm applied to large-space constraint satisfaction

The paper treats a fuzzy-neural tuned genetic algorithm for solving a constraint satisfaction problem for an industrial application. It describes the design of a reflecting lamp composed of five consecutive straight mirror segments that satisfy both illumination efficiency and uniformity properties. An analytically established neural network dynamically controls the genetic algorithm mutation rate and the convergence criteria. The neural network implements a six-rule fuzzy system that gains its knowledge from a human operator and works in a similar way to monitor the convergence process. Using numerical experiments the lamp configurations are determined. The proposed method can also be applied to other optimization design tasks.