Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Complexity in Adaptive Systems

Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_148

Synonyms

Definition

An  adaptive system, or complex adaptive system, is a special case of complex systems, which is able to adapt its behavior according to changes in its environment or in parts of the system itself. In this way, the system can improve its performance through a continuing interaction with its environment. The concept of complexity in an adaptive system is used to analyze the interactive relationship between the system and its environment, which can be classified into two types: internal complexity for model complexity, and external complexity for data complexity. The internal complexity is defined by the amount of input, information, or energy that the system receives from its environment. The external complexity refers to the complexity of how the system represents these inputs through its internal process.

Motivation and Background

Adaptive systems range from natural systems to artificial systems (Holland, 19921995; Waldrop, ...

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Recommended Reading

  1. Gell-Mann, M., & Lloyd, S. (1996). Information measures, effective complexity, and total information. Complexity, 2(1), 44–52.MathSciNetGoogle Scholar
  2. Holland, J. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press.Google Scholar
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  8. Vapnik, V. (1998). Statistical learning theory. New York: John Wiley & Sons.MATHGoogle Scholar
  9. Waldrop, M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schuster.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jun He

There are no affiliations available