Intelligent bionic genetic algorithm (IB-GA) and its convergence

Intelligent bionic genetic algorithm (IB-GA) and its convergence
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智能仿生遗传算法(IB-GA)及其收敛性

DOI:
10.1016/j.eswa.2011.01.091
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发表时间:
2011-07
影响因子:
8.5
通讯作者:
Fachao Li, Li Da Xu, Chenxia Jin, Hong Wang
Fachao Li, Li Da Xu, Chenxia Jin, Hong Wang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Fachao Li, Li Da Xu, Chenxia Jin, Hong Wang

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遗传算法作为一种新型的智能优化方法,具有结构简单、适应性强等特点,在许多真实的应用中取得了巨大的成功。然而,它有许多缺点,如较大的计算复杂度和更多的机会被困在局部状态。从提高进化效率的角度出发,分析了现有遗传操作的不足和生物进化的本质特征,提出了基于变异准则函数的复合变异策略、基于智能进化的多保留策略和反映不同进化模式的弱算术交叉策略。在此基础上,提出了一种具有结构特征的智能仿生遗传算法(简称IB-GA)。最后,我们用马尔可夫链理论和仿真技术分析了IB-GA的性能。结果表明,IB-GA本质上是普通GA的扩展,在计算效率和收敛性能方面明显优于普通GA。
As a new kind of intelligence optimization method, genetic algorithms, with the features of simple structure and strong adaptability, achieves great success in many real applications. However, it has many shortcomings such as a greater computation complexity and more chance of being trapped in local states. In this paper, through analyzing the deficiency of the existing genetic operation and the essential characteristics of creature evolution from the angle of improving evolution efficiency, we propose a compound mutation strategy based on mutation criteria function, a multi-reserved strategy based on intelligence evolution, and a weak arithmetic crossover strategy reflecting different evolution modes. Furthermore, we establish an intelligent bionic genetic algorithm with structural features (denoted by IB-GA, for short). Finally, we analyze the performances of IB-GA with the theory of Markov chains and simulation technology. The results indicate that IB-GA is essentially an extension of ordinary GA and obviously better than ordinary GA in terms of computation efficiency and convergence performance.
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