TY - CHAP
T1 - Immune system support for scheduling
AU - Lee, Young Choon
AU - Zomaya, Albert Y.
N1 - 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 https://doi.org/10.1007/978-1-84628-982-8_11
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85032186541&partnerID=8YFLogxK
U2 - 10.1007/978-1-4471-5113-5_11
DO - 10.1007/978-1-4471-5113-5_11
M3 - Chapter
AN - SCOPUS:85032186541
SN - 9781447151128
T3 - Advanced Information and Knowledge Processing
SP - 295
EP - 319
BT - Advances in applied self-organizing systems
A2 - Prokopenko, Mikhail
PB - Springer, Springer Nature
CY - London
ER -