A human-simulated immune evolutionary computation approach
Contribuinte(s) |
Huang, T. |
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Data(s) |
01/11/2012
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
Springer |
Relação |
http://eprints.qut.edu.au/53852/6/53852.pdf http://link.springer.com/chapter/10.1007%2F978-3-642-34487-9_12 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. |
Direitos |
Copyright 2012 Springer. |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080108 Neural Evolutionary and Fuzzy Computation #080299 Computation Theory and Mathematics not elsewhere classified #080399 Computer Software not elsewhere classified #Human-simulated intelligence #Artificial immune systems #Evolutionary algorithm #Clonal selection #Evolutionary operators |
Tipo |
Conference Paper |