Immune system support for scheduling

Young Choon Lee*, Albert Y. Zomaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter


Haven’t there been enough approaches to scheduling problems? In terms of variety the answer might be ‘yes’. However, the answer is not as straightforward in terms of effectiveness. In many scheduling problems, it is highly improbable, if not impossible, to obtain optimal schedules within a reasonable amount of time in spite of adopting a wide range of approaches, including evolutionary computation (EC), artificial neural networks (ANN), fuzzy systems (FS), simulated annealing (SA) and Tabu search (TS). In recent years attention has been drawn to another biologically-inspired computing paradigm called artificial immune systems (AIS). An AIS abstracts and models the principles and processes of the biological immune system in order to effectively tackle challenging problems in dynamic environments. Major AIS models include negative selection, clonal selection, immune networks and more recently danger theory (Garrett in Evol. Comput. 13(2):145–177, 2005). There are some similarities between these principles and processes in the immune system and those found in other nature-inspired computing approaches, EC and ANN in particular. However, there are also substantial differences. In particular, adaptive cloning and mutation processes make AIS distinctive and useful.

Original languageEnglish
Title of host publicationAdvances in applied self-organizing systems
EditorsMikhail Prokopenko
Place of PublicationLondon
PublisherSpringer, Springer Nature
Number of pages25
ISBN (Electronic)9781447151135
ISBN (Print)9781447151128
Publication statusPublished - 2013
Externally publishedYes

Publication series

NameAdvanced Information and Knowledge Processing
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441

Bibliographical note

Originally published in Lee Y.C., Zomaya A.Y. (2008) Immune System Support for Scheduling. (pp 247-270) In: Prokopenko M. (eds) Advances in Applied Self-organizing Systems. Advanced Information and Knowledge Processing. Springer, London. ISBN: 978-1-84628-981-1 DOI

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