Fuzzy methods of driving genetic algorithms

This article presents two concepts of modified genetic algorithms, they employ a fuzzy logic controller to set a trend individuals' evolution. In the algorithms we use a fuzzy logic controller, evaluating each individual as a parent for the next population. The fuzzy logic controller evaluates all individuals using fitness functions for earlier populations, which help's to keep the knowledge collected in the prior populations. The controller modifies the probability of selection to parents' pool, or probability of mutation, so in the fuzzy controlled genetic algorithms, a number of better quality individuals are larger then in the elementary genetic algorithms. We use the traveling salesman problem (TSP) as illustrations.