Abstract
Evolving systems emerge from the synergy between systems with adaptive structures, and the recursive methods of machine learning. Evolving algorithms construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components can be chosen to assemble the system structure, rules, trees, and neural networks being amongst the most prominent. Evolving systems concern mainly with time-varying environments, and processing of nonstationary stream data using computationally efficient recursive algorithms. They are particularly suitable for on-line, real-time applications, and dynamically changing situations, and operating conditions. This chapter gives an overview of evolving systems focusing on the model components, learning algorithms, and illustrative applications. The aim of to introduce the main ideas and a state of the art view of the area.
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Gomide, F., Lemos, A., Caminhas, W. (2021). Evolving Systems. In: Lesot, MJ., Marsala, C. (eds) Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications. Studies in Fuzziness and Soft Computing, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-54341-9_15
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