Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddPennod

205 Wedi eu Llwytho i Lawr (Pure)


Artificial immune systems are a class of nature-inspired algorithms based on the immune system of vertebrates. They have been used in a large number of different areas of application, most prominently learning, classification, pattern recognition, and (function) optimization. In the context of optimization, clonal selection algorithms are the most popular and constitute an interesting and promising alternative to evolutionary algorithms. While structurally similar, they offer very different features and capabilities. Over the last decade, significant progress has been made in the theoretical foundations of clonal selection algorithms. This chapter gives an overview of the state of the art in the theory of artificial immune systems with a focus on optimization. It provides pointers to corresponding articles where more details and proofs can be found.
Iaith wreiddiolSaesneg
TeitlTheory of Evolutionary Computation
Is-deitlRecent Developments in Discrete Optimization
GolygyddionB. Doerr, F. Neumann
CyhoeddwrSpringer Nature
Nifer y tudalennau32
ISBN (Electronig)978-3-030-29414-4
ISBN (Argraffiad)978-3-030-29413-7
StatwsE-gyhoeddi cyn argraffu - 21 Tach 2019

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