utility of each granule and the overall population fitness. To encourage fewer function evaluations, each granule's radius of influence is initially large and is gradually shrunk in latter stages of evolution. This encourages more exact fitness evaluations when competition is fierce among more similar and converging solutions. Furthermore, to prevent the pool from growing too large, granules that are not used are gradually eliminated. Additionally, AFFG mirrors two features of human cognition: (a) granularity (b) similarity analysis. This granulation-based fitness approximation scheme is applied to solve various engineering optimization problems including detecting hidden information from a watermarked signal in addition to several structural optimization problems. See also A complete list of references on Fitness Approximation in Evolutionary Computation, by Yaochu Jin. The cyber shack of Adaptive Fuzzy Fitness Granulation (AFFG) That is designed to accelerate the convergence rate of EAs. References Evolutionary algorithms Genetic algorithms ca:Funció d'aptitud (algorisme genètic) de:Fitnessfunktion nl:Fitnessfunctie ja:適応度関数