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SIGA: a novel self-adaptive immune genetic algorithm. (Chinese. English summary) Zbl 1199.68532

Summary: This paper proposes a novel self-adaptive genetic algorithm SIGA (Self-adaptive Immune Genetic Algorithm) based on immunity to overcome the shortage of traditional genetic algorithms that the converging speed is slow and the solution is a local optimum. The algorithm improves the genetic operators and proposes self-adaptive crossover and mutation operators in case of keeping individual diversity and avoiding prematurity. An immune selection algorithm based on selection probability of similarity and vector distance in order to keep individual diversity and improve the level of fitness is proposed. The results of the experiments indicate that SIGA can improve the converging speed by three to ninety times, enhance the precision which reaches to \(10^{-3}\), and avoid prematurity to some extent ones compared with traditional genetic algorithms and immune algorithms.

MSC:

68W05 Nonnumerical algorithms
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence

Software:

SIGA
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