Institute of Computer Science III
University of Bonn
Databases * Information Systems * Software Engineering *
Pattern Recognition * Image Processing * Artificial Intelligence * Robotics
Adaptive Steuerung von Strategieparametern bei genetischen Algorithmen
The practical application of systems based on evolutionary concepts is often
complicated by the problem of tuning the system parameters. Depending on the
parameter setting the evolutionary algorithm converges prematurely or makes
a long term random walk through the solution space. A successful problem
solving process is only guaranteed on the small edge between these extremes.
This report gives an overview of genetic algorithms and evolution strategies
and discusses several control techniques for their strategy parameters.
A new method is presented applying fuzzy control rules for the
dynamic guidance of a genetic algorithm. This approach allows the
incorporation of available knowledge about reasonable parameter settings.
The fuzzy control rules adjust the inversion, crossover and mutation rate to
achieve a desired population diversity. Experimental results of a comparison
with an (1+1)-evolution strategy demonstrate that fuzzy control allows better
approximation of the target entropy and results in better performance of the
underlying genetic algorithm.
Click here to obtain the full paper (PS, gzip, 328413 bytes, 48 pages, german language)
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16.12.05