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- Publisher Website: 10.1007/978-3-642-33757-4_6
- Scopus: eid_2-s2.0-84866396800
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Conference Paper: Clustering-based multi-objective immune optimization evolutionary algorithm
Title | Clustering-based multi-objective immune optimization evolutionary algorithm |
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Authors | |
Keywords | Artificial immune systems Evolutionary algorithm Multi-objective optimization Evolution process Immune optimization |
Issue Date | 2012 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | The 11th International Conference on Artificial Immune Systems (ICARIS 2012), Taormina, Italy, 28-31 August 2012. In Lecture Notes in Computer Science, 2012, v. 7597, p. 72-85 How to Cite? |
Abstract | In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods - artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms. © 2012 Springer-Verlag. |
Description | LNCS v. 7597 has title: Artificial Immune Systems: 11th International Conference, ICARIS 2012 ... proceedings |
Persistent Identifier | http://hdl.handle.net/10722/165333 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Tsang, WWP | en_US |
dc.contributor.author | Lau, HYK | en_US |
dc.date.accessioned | 2012-09-20T08:17:16Z | - |
dc.date.available | 2012-09-20T08:17:16Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | The 11th International Conference on Artificial Immune Systems (ICARIS 2012), Taormina, Italy, 28-31 August 2012. In Lecture Notes in Computer Science, 2012, v. 7597, p. 72-85 | en_US |
dc.identifier.isbn | 978-3-642-33756-7 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/165333 | - |
dc.description | LNCS v. 7597 has title: Artificial Immune Systems: 11th International Conference, ICARIS 2012 ... proceedings | - |
dc.description.abstract | In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods - artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms. © 2012 Springer-Verlag. | - |
dc.language | eng | en_US |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | - |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.subject | Artificial immune systems | - |
dc.subject | Evolutionary algorithm | - |
dc.subject | Multi-objective optimization | - |
dc.subject | Evolution process | - |
dc.subject | Immune optimization | - |
dc.title | Clustering-based multi-objective immune optimization evolutionary algorithm | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Tsang, WWP: h0246582@hku.hk | en_US |
dc.identifier.email | Lau, HYK: hyklau@hkucc.hku.hk | - |
dc.identifier.authority | Lau, HYK=rp00137 | en_US |
dc.identifier.doi | 10.1007/978-3-642-33757-4_6 | - |
dc.identifier.scopus | eid_2-s2.0-84866396800 | - |
dc.identifier.hkuros | 209415 | en_US |
dc.identifier.volume | 7597 | - |
dc.identifier.spage | 72 | - |
dc.identifier.epage | 85 | - |
dc.publisher.place | Germany | - |
dc.customcontrol.immutable | sml 130515 | - |
dc.identifier.issnl | 0302-9743 | - |