Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Fitness Inheritance In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Structure optimization of neural networks for evolutionary design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
The Fast Evaluation Strategy for Evolvable Hardware
Genetic Programming and Evolvable Machines
Is fitness inheritance useful for real-world applications?
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive genetic operators based on coevolution with fuzzybehaviors
IEEE Transactions on Evolutionary Computation
Genetic evolution of radial basis function coverage using orthogonal niches
IEEE Transactions on Neural Networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary hidden information detection by granulation-based fitness approximation
Applied Soft Computing
Design of fuzzy radial basis function-based polynomial neural networks
Fuzzy Sets and Systems
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Nature may have been the original inspiration for evolutionary algorithms, but unlike artificially designed systems, nature has an abundance of resources and time. For man-made systems, computational complexity is a prohibitive factor in sufficiently large and complex problems of today. Much of this computational complexity is due to the fitness function evaluation that may either not exist or be computationally very expensive. But, an exact computation of fitness may not be really necessary as long as a proper rank is approximately preserved in the evolution's scheme of the survival of the fittest. Here, we aim to exploit this feature of evolution and to investigate the use of fitness granulation via an adaptive fuzzy similarity analysis in order to reduce the number of fitness evaluations. In the proposed algorithm, an individual's fitness is only computed if it has insufficient similarity to a pool of fuzzy granules whose fitness has already been computed. If an individual is sufficiently similar to a known fuzzy granule, then that granule's fitness is used instead as a crude estimate. Otherwise, that individual is added to the pool as the core of a new fuzzy granule. Each granule's radius of influence is adaptive and will grow/shrink depending on the population fitness. The proposed technique is applied to two sets of problems. First is a set of several numerical benchmark problems with various optimization characteristics. Second is a set of four hardware design problems that are evaluated via finite element analysis. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fast evolutionary strategy (FES), a GA-NN, as well as a simple GA, in terms of both computational efficiency and accuracy. Statistical analysis reveals that the proposed method significantly decreases the number of fitness function evaluations while finding equally good or better solutions. Moreover, application to the hardware design problems reveals better structural designs more consistently with better computational efficiency.