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Self-Evolvable Systems [electronic resource] : Machine Learning in Social Media / by Octavian Iordache.

By: Iordache, Octavian [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Understanding Complex Systems: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XXI, 275 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642288821.Subject(s): Engineering | Physics | Engineering | Complexity | Computational Intelligence | Nonlinear DynamicsDDC classification: 620 Online resources: Click here to access online
Contents:
Introduction -- General Framework -- Differential Models -- Informational Criteria -- Self-Evolvability for Physical and Chemical Systems -- Self-Evolvability for Biosystems -- Self-Evolvability for Cognitive Systems -- Control Systems -- Manufacturing Systems -- Concept Lattices -- Design of Experiments -- Perspectives.
In: Springer eBooksSummary: This monograph presents key method to successfully manage the growing  complexity of systems  where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced and challenges and limitations of the self-evolvable engineering systems are evaluated.
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Introduction -- General Framework -- Differential Models -- Informational Criteria -- Self-Evolvability for Physical and Chemical Systems -- Self-Evolvability for Biosystems -- Self-Evolvability for Cognitive Systems -- Control Systems -- Manufacturing Systems -- Concept Lattices -- Design of Experiments -- Perspectives.

This monograph presents key method to successfully manage the growing  complexity of systems  where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced and challenges and limitations of the self-evolvable engineering systems are evaluated.

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