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Through series of experiments this work compares effects of different types of genetic algorithms on evolution of a neural network that is used to control a robot. Genetic algorithms using binary and real coded individuals, algorithms using basic and advanced mutations and crossovers and algorithms using fixed and variable population size are compared on three tasks of evoltionary robotics. The goal is to determine wether usage of advanced genetic algorithms leads to faster convergence or to better solution than usage of basic genetic algorithm. Experiments are performed in an easily extendable simulator developed for purposes of this work.