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Figure
9-1: Shows the spatial wave and time domain on a CNN, which connected to the robot
actuators
A fitness function is a particular type of objective function that quantifies the optimality of
a solution. Input data for the fitness function is based on measurements of robot parts
orientation, location and displacement. In fitness function we don’t define any behavioral
locomotion exactly. On the other hand, we define a function that satisfies the target or
destination. With this method based on genetic algorithms, an optimum template ensures
that the robot can move or act according to our desires. The most important point in this
learning method is that we don’t predefine any robot kinematics for movement in the
fitness function.
