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- W2372445020 abstract "This paper presents a clustering analysis method of immune genetic algorithm based on entropy. Immune algorithm is introduced into genetic algorithm toincorporate immune algorithm’s functions such as immune memory, self-adjustability and the keeping of the diversity. The immune-genetic algorithm based on information entropy can overcome the shortcomings of standard genetic algorithm, e. g., poor local search capability, excessive computational cost and premature convergence, etc. This algorithm adopts DNA to code the antibody, and uses information entropy to denote the affinity and the consistence of the antibodies. The converged affinity is presented to achieve self-adjustability of colony, which keeps the diversity of colony. The experiment demonstrates that the algorithm is better than standard genetic algorithm in clustering analysis." @default.
- W2372445020 created "2016-06-24" @default.
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- W2372445020 date "2008-01-01" @default.
- W2372445020 modified "2023-09-24" @default.
- W2372445020 title "Clustering Analysis of Immune-genetic Algorithm Based on Information Entropy" @default.
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