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Organic Computing pp 141–166Cite as

Genesis of Organic Computing Systems: Coupling Evolution and Learning

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Part of the book series: Understanding Complex Systems ((UCS))

Summary

Organic computing calls for efficient adaptive systems in which flexibility is not traded in against stability and robustness. Such systems have to be specialized in the sense that they are biased towards solving instances from certain problem classes, namely those problems they may face in their environment. Nervous systems are perfect examples. Their specialization stems from evolution and development. In organic computing, simulated evolutionary structure optimization can create artificial neural networks for particular environments. In this chapter, trends and recent results in combining evolutionary and neural computation are reviewed. The emphasis is put on the influence of evolution evolution and development on the structure of neural systems. It is demonstrated how neural structures can be evolved that efficiently learn solutions for problems from a particular problem class. Simple examples of systems that “learn to learn” as well as technical solutions for the design design of turbomachinery components are presented.

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  1. Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany

    Christian Igel

  2. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach, Germany

    Bernhard Sendhoff

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  1. Christian Igel
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Igel, C., Sendhoff, B. (2009). Genesis of Organic Computing Systems: Coupling Evolution and Learning. In: Organic Computing. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77657-4_7

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