This Webpage contains additional material to the paper:

M. Davarynejad, M.-R. Akbarzadeh-T, N. Pariz, "A Novel General Framework for Evolutionary Optimization: Adaptive Fuzzy Fitness Granulation", Proceedings of the 2007 IEEE International Conference on Evolutionary Computing, (CEC'2007), pp. 951--956, Singapore, September 25-28, 2007.

and partially covers the following paper:

M. Davarynejad, C.W. Ahn, J. Vrancken, J. van den Berg, C.A. Coello Coello, "Evolutionary hidden information detection by granulation-based fitness approximation", Applied Soft Computing, Vol. 10(3), pp. 719--729, 2010, DOI: 10.1016/j.asoc.2009.09.001. '

For a complete list of references on Fitness Approximation in Evolutionary Computation please take a look this link, maintained by Yaochu Jin.


Adaptive Fuzzy Fitness Granulation (AFFG):


A method for accelerating the convergence rate of evolutionary computing



In lots of real word optimization problems including engineering problems, the optimization cost is dominated by number of fitness function evaluations needed to obtain a good solution. In order to obtain efficient optimization algorithms, it is crucial to use information gained during optimization process. This leads to build model of the fitness function to choose 'smart' search steps. The model replaces expensive fitness function evaluations with cheap model evaluations. AFFG is a novel granulation based fitness approximation scheme that proposed in order to approximate the fitness function for substituting the time consuming large-scale problem analysis (L-SPA) by FEA.