Genesis of organic computing systems: coupling evolution and learning

Christian Igel*, Bernhard Sendhoff

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationOrganic computing
Number of pages26
Publication date2008
Pages141-166
ISBN (Print)978-3-540-77656-7
ISBN (Electronic)978-3-540-77657-4
DOIs
Publication statusPublished - 2008
Externally publishedYes
SeriesUnderstanding Complex Systems
ISSN1860-0832

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