渡线
遗传算法
计算机科学
适应度函数
人口
算法
功能(生物学)
P系统
任务(项目管理)
膜计算
人工神经网络
人工智能
机器学习
工程类
进化生物学
生物
社会学
人口学
系统工程
作者
Jianping Dong,Michael Stachowicz,Gexiang Zhang,Matteo Cavaliere,Haina Rong,Prithwineel Paul
摘要
At present, all known spiking neural P systems (SN P systems) are established by manual design rather than automatic design. The method of manual design poses two problems: consuming a lot of computing time and making unnecessary mistakes. In this paper, we propose an automatic design approach for SN P systems by genetic algorithms. More specifically, the regular expressions are changed to achieve the automatic design of SN P systems. In this method, the number of neurons in system, the synapse connections between neurons, the number of rules within each neuron and the number of spikes within each neuron are known. A population of SN P systems is created by generating randomly accepted regular expressions. A genetic algorithm is applied to evolve a population of SN P systems toward a successful SN P systems with high accuracy and sensitivity for carrying out specific task. An effective fitness function is designed to evaluate each candidate SN P system. In addition, the elitism, crossover and mutation are also designed. Finally, experimental results show that the approach can successfully accomplish the automatic design of SN P systems for generating natural numbers and even natural numbers by using the .NET framework.
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