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In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules.[1] The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems.[2]
Intelligent systems have to be able to evolve, self-develop, and self-learn continuously in order to reflect a dynamically evolving environment. The concept of Evolving Intelligent Systems (EISs) was conceived around the turn of the century[3][4][5][6][7][8][9] with the phrase EIS itself coined for the first time by Angelov and Kasabov in a 2006 IEEE newsletter[8] and expanded in a 2010 text.[9] EISs develop their structure, functionality and internal knowledge representation through autonomous learning from data streams generated by the possibly unknown environment and from the system self-monitoring.[10] EISs consider a gradual development of the underlying (fuzzy or neuro-fuzzy) system structure and differ from evolutionary and genetic algorithms which consider such phenomena as chromosomes crossover, mutation, selection and reproduction, parents and off-springs. The evolutionary fuzzy and neuro systems are sometimes also called "evolving"[11][12][13] which leads to some confusion. This was more typical for the first works on this topic in the late 1990s.
^Nikola K. Kasabov (23 August 2007). Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer Science & Business Media. p. 9. ISBN 978-1-84628-347-5.
^Plamen P. Angelov; Xiaowei Gu (17 October 2018). Empirical Approach to Machine Learning. Springer. pp. 51–. ISBN 978-3-030-02384-3.
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