A human-simulated immune evolutionary computation approach

Authors:
Gang Xie;Hong-Bo Guo;Yu-Chu Tian;Maolin Tang
Affiliations:
School of Elec Eng and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia,College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. C ...;College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. China;School of Elec Eng and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia;School of Elec Eng and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

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Abstract

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.