Abstract
In this paper, the Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. This paper synthesizes the advantages of clonal selection algorithm and proposed optimal design problem using clonal selection algorithm which is a basis of the immune system. CSA, the essence of immune algorithm, is effective to solve optimal problem. The clonal selection algorithm is highly parallel and presents a fine tractability in terms of computational cost. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution. Clonal selection algorithm and genetic algorithm are used to reach the optimization performances for two numerical function. Then those results are compared each other. These proposed algorithms are shown to be an evolutionary strategy capable of solving optimal design problem.
| Original language | English |
|---|---|
| Title of host publication | Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings |
| Pages | 604-610 |
| Number of pages | 7 |
| Publication status | Published - 2005 Dec 1 |
| Event | 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia Duration: 2005 Sep 14 → 2005 Sep 16 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3681 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 05-09-14 → 05-09-16 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Optimal design using clonal selection algorithm. / Su, Yi Hui; Shyr, Wen Jye; Su, Te Jen.
Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings. 2005. p. 604-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Optimal design using clonal selection algorithm
AU - Su, Yi Hui
AU - Shyr, Wen Jye
AU - Su, Te Jen
PY - 2005/12/1
Y1 - 2005/12/1
N2 - In this paper, the Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. This paper synthesizes the advantages of clonal selection algorithm and proposed optimal design problem using clonal selection algorithm which is a basis of the immune system. CSA, the essence of immune algorithm, is effective to solve optimal problem. The clonal selection algorithm is highly parallel and presents a fine tractability in terms of computational cost. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution. Clonal selection algorithm and genetic algorithm are used to reach the optimization performances for two numerical function. Then those results are compared each other. These proposed algorithms are shown to be an evolutionary strategy capable of solving optimal design problem.
AB - In this paper, the Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. This paper synthesizes the advantages of clonal selection algorithm and proposed optimal design problem using clonal selection algorithm which is a basis of the immune system. CSA, the essence of immune algorithm, is effective to solve optimal problem. The clonal selection algorithm is highly parallel and presents a fine tractability in terms of computational cost. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution. Clonal selection algorithm and genetic algorithm are used to reach the optimization performances for two numerical function. Then those results are compared each other. These proposed algorithms are shown to be an evolutionary strategy capable of solving optimal design problem.
UR - http://www.scopus.com/inward/record.url?scp=33745321000&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745321000&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:33745321000
SN - 3540288945
SN - 9783540288947
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 604
EP - 610
BT - Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
ER -