Theoretical Foundations of Immune-Inspired Randomized Search Heuristics for Optimization

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Abstract

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.
Original languageEnglish
Title of host publicationTheory of Evolutionary Computation
Subtitle of host publicationRecent Developments in Discrete Optimization
EditorsB. Doerr, F. Neumann
PublisherSpringer Nature
Pages443-474
ISBN (Electronic)978-3-030-29414-4
ISBN (Print)978-3-030-29413-7
Publication statusPublished - 03 Dec 2020

Publication series

NameNatural Computing Series
PublisherSpringer Nature

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