A human-simulated immune evolutionary computation approach

Xie, Gang, Guo, H. B., Tian, Yu-Chu, & Tang, Maolin (2012) A human-simulated immune evolutionary computation approach. In Huang, T. (Ed.) Lecture Notes in Computer Science, Springer, Doha, Qatar, pp. 92-99.

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Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

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ID Code: 53852
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Human-simulated intelligence, Artificial immune systems, Evolutionary algorithm, Clonal selection, Evolutionary operators
ISSN: 0302-9743
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200) > Computation Theory and Mathematics not elsewhere classified (080299)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Computer Software not elsewhere classified (080399)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer.
Deposited On: 27 Sep 2012 04:45
Last Modified: 14 Feb 2013 11:41

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