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For the actual need of future research and application, this paper proposes a new method that is a new fuzzy control system of fuzzy integral-genetic algorithm (FI-GA). By fuzzy integral, it can study comprehensive evaluation of population diversity and individual quantity on three attributes: individual difference extent, the difference extent of individual's fitness and the difference extent of...
Mutation plays an important role in the computing of genetic algorithm. In this study, the relationship between success probability and mutation rate is analyzed by using multiplicative landscape. The success probability is defined that there is at least one optimal solution in the stationary state. The effect of mutation was illustrated by studying the distribution of the first order schemata with...
The performance of evolutionary algorithms (EAs) may be enhanced whether the choice of some parameters, as mutation rate and crossover method, is made appropriately. Several methods to adjust those parameters have been developed in order to enhance EAs performance. For this reason, it is important to understand EA dynamics. This paper presents a new population dynamics model to describe and predict...
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