摘要
提出了应用神经网络实现遗传算法的模型,将普通遗传算法中交叉操作和突变操作的概念进行推广,并提出了全交叉和多点突变的概念以及实现这两种操作的人工神经元模型。通过一组著名的测试函数将该算法与典型遗传算法就求解优化问题的性能作了比较研究。此研究对用硬件执行遗传算法,显式地实现遗传算法的内在并行性,从而提高遗传算法的实时性,拓宽遗传算法的应用领域具有重要的意义。
The authors present an idea of implementing genetic algorithm in neural network so as to break the time-consuming bottleneck of GA (genetic algorithm). They propose the architecture of the NN (neural network) based model for GA, and generalize both the crossover operation and mutation operation to the so-called all-crossover and multi-point mutation operations. Through a group famous test functions, they compare this algorithm and typical genetic algorithm in optimization of solution search. This study is significant for using hardware to implement GA and explicitly realizing the inherent parallelism of GA so as to improve the real time performance of GA and broaden the application range of GA.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2003年第2期175-178,共4页
Journal of Beijing University of Technology
基金
国家自然科学基金(60234020)
国家自然科学基金(50274003)
关键词
遗传算法
神经网络
遗传操作算子
优化计算
硬件实现
genetic algorithm
neural network
genetic operator
optimization computation
hardware implement
