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Evolutionary Design of a Control Architecture for Soccer-Playing Robots 217

error

Fig. 22. Average and maximal error for a feed-forward back-propagation network
with two hidden layers as a function of the two numbers of hidden neurons

100
average error learn values
average error test values
10
average error 0.1 1

0.01
1 10 100 1000 10000 100000 1000000 10000000
learning cycles
Fig. 23. Typical difference between the training and test error during the course of
learning

5 Path Planning using Genetic Algorithms

This section demonstrates how genetic-algorithm-based path planning can be
employed on a RoboCup robot. It further demonstrates that a first solution
is continuously updated to a changing environment.
The purpose of path planning algorithms is to find a collision free route
that satisfies certain optimization parameters between two points. In dynamic
environments, a found solution needs to be re-evaluated and updated to envi-
ronmental changes.
In case of RoboCup, all robots on the field are obstacles. Due to the global
camera view, the positions of all robots and hereby all obstacles are known
by the robot.
Genetic algorithms use evolutionary methods to find an optimal solution.
The solution space is formed by parameters. Possible solutions are repre-
sented as individuals of a population. Each gene of an individual represents