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.

Automatic design of spiking neural p systems based on genetic algorithms / Dong, J.; Stachowicz, M.; Zhang, G.; Cavaliere, M.; Rong, H.; Paul, P.. - In: INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING. - ISSN 1548-7199. - 16:2-3(2021), pp. 201-216.

Automatic design of spiking neural p systems based on genetic algorithms

Cavaliere M.;
2021

Abstract

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.
2021
16
2-3
201
216
Automatic design of spiking neural p systems based on genetic algorithms / Dong, J.; Stachowicz, M.; Zhang, G.; Cavaliere, M.; Rong, H.; Paul, P.. - In: INTERNATIONAL JOURNAL OF UNCONVENTIONAL COMPUTING. - ISSN 1548-7199. - 16:2-3(2021), pp. 201-216.
Dong, J.; Stachowicz, M.; Zhang, G.; Cavaliere, M.; Rong, H.; Paul, P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1319956
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