Automatic Design of Spiking Neural P Systems Based on Genetic Algorithms.
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| Title: | Automatic Design of Spiking Neural P Systems Based on Genetic Algorithms. |
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| Authors: | JIANPING DONG1, STACHOWICZ, MICHAEL2, GEXIANG ZHANG1 zhgxdylan@126.com, CAVALIERE, MATTEO2, HAINA RONG3, PAUL, PRITHWINEEL3 |
| Source: | International Journal of Unconventional Computing. 2021, Vol. 16 Issue 2/3, p201-216. 16p. |
| Subjects: | Genetic algorithms, Microsoft .NET Framework, Natural numbers, Action potentials, Number systems, Elitism |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Unconventional Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 148711592 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automatic Design of Spiking Neural P Systems Based on Genetic Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22JIANPING+DONG%22">JIANPING DONG</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22STACHOWICZ%2C+MICHAEL%22">STACHOWICZ, MICHAEL</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22GEXIANG+ZHANG%22">GEXIANG ZHANG</searchLink><relatesTo>1</relatesTo><i> zhgxdylan@126.com</i><br /><searchLink fieldCode="AR" term="%22CAVALIERE%2C+MATTEO%22">CAVALIERE, MATTEO</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22HAINA+RONG%22">HAINA RONG</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22PAUL%2C+PRITHWINEEL%22">PAUL, PRITHWINEEL</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Unconventional+Computing%22">International Journal of Unconventional Computing</searchLink>. 2021, Vol. 16 Issue 2/3, p201-216. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Microsoft+%2ENET+Framework%22">Microsoft .NET Framework</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+numbers%22">Natural numbers</searchLink><br /><searchLink fieldCode="DE" term="%22Action+potentials%22">Action potentials</searchLink><br /><searchLink fieldCode="DE" term="%22Number+systems%22">Number systems</searchLink><br /><searchLink fieldCode="DE" term="%22Elitism%22">Elitism</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Unconventional Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 201 Subjects: – SubjectFull: Genetic algorithms Type: general – SubjectFull: Microsoft .NET Framework Type: general – SubjectFull: Natural numbers Type: general – SubjectFull: Action potentials Type: general – SubjectFull: Number systems Type: general – SubjectFull: Elitism Type: general Titles: – TitleFull: Automatic Design of Spiking Neural P Systems Based on Genetic Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: JIANPING DONG – PersonEntity: Name: NameFull: STACHOWICZ, MICHAEL – PersonEntity: Name: NameFull: GEXIANG ZHANG – PersonEntity: Name: NameFull: CAVALIERE, MATTEO – PersonEntity: Name: NameFull: HAINA RONG – PersonEntity: Name: NameFull: PAUL, PRITHWINEEL IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 15487199 Numbering: – Type: volume Value: 16 – Type: issue Value: 2/3 Titles: – TitleFull: International Journal of Unconventional Computing Type: main |
| ResultId | 1 |
