Locating controlling regions of neural networks using constrained evolutionary computation
Detection of controlling regions/driver nodes of the cortical networks helps the networks dynamics reach a desired state. Controllability of the complex networks can be accomplished through minimizing two quantities related to the eigenvalues of the extended adjacency matrix. The identification prob...
Uložené v:
| Vydané v: | IEEE transactions on evolutionary computation s. 1581 - 1588 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English Japanese |
| Vydavateľské údaje: |
IEEE
01.05.2015
|
| Predmet: | |
| ISSN: | 1089-778X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Detection of controlling regions/driver nodes of the cortical networks helps the networks dynamics reach a desired state. Controllability of the complex networks can be accomplished through minimizing two quantities related to the eigenvalues of the extended adjacency matrix. The identification problem of the driver nodes can be solved as a Constrained Optimization Problem which unifies these two quantities into one framework. The cat cortical network is taken as an example of a directed weighted complex network. In this paper, the Constrained Dynamic Differential Evolution (CDDE) algorithm is generalized to produce the Generalized Constrained Dynamic Differential Evolution (GCDDE) algorithm. The GCDDE uses the exploitation probability Pe to determine whether the crossover rate CR takes random large values from the range [0.5, 1] to produce different levels of exploration ability or takes random small values from the range [0, 0.5] to produce different levels of exploitation ability. Through the value of Pe, GCCDE can attain the tradeoff between exploration and exploitation with multiformity. Then, the algorithms GCDDE and CDDE are applied to determine the controlling regions of the cortical networks. The results illustrate that GCDDE outperforms four state-of-the-art Constrained Optimization Evolutionary Algorithms and also approaches of the control theory and graph theory. Using GCDDE, the identification problem of driver nodes is investigated in a macroscopic manner. It is found that the controlling regions have a high in-degree and a low out-degree. It is important to mention that when the number of driver nodes increases, the GCDDE can find feasible optimal solutions that make the cat cortical network more controllable. |
|---|---|
| AbstractList | Detection of controlling regions/driver nodes of the cortical networks helps the networks dynamics reach a desired state. Controllability of the complex networks can be accomplished through minimizing two quantities related to the eigenvalues of the extended adjacency matrix. The identification problem of the driver nodes can be solved as a Constrained Optimization Problem which unifies these two quantities into one framework. The cat cortical network is taken as an example of a directed weighted complex network. In this paper, the Constrained Dynamic Differential Evolution (CDDE) algorithm is generalized to produce the Generalized Constrained Dynamic Differential Evolution (GCDDE) algorithm. The GCDDE uses the exploitation probability Pe to determine whether the crossover rate CR takes random large values from the range [0.5, 1] to produce different levels of exploration ability or takes random small values from the range [0, 0.5] to produce different levels of exploitation ability. Through the value of Pe, GCCDE can attain the tradeoff between exploration and exploitation with multiformity. Then, the algorithms GCDDE and CDDE are applied to determine the controlling regions of the cortical networks. The results illustrate that GCDDE outperforms four state-of-the-art Constrained Optimization Evolutionary Algorithms and also approaches of the control theory and graph theory. Using GCDDE, the identification problem of driver nodes is investigated in a macroscopic manner. It is found that the controlling regions have a high in-degree and a low out-degree. It is important to mention that when the number of driver nodes increases, the GCDDE can find feasible optimal solutions that make the cat cortical network more controllable. |
| Author | Shoukry, Amin A. Eita, Mohammad A. Shibuya, Tetsuo |
| Author_xml | – sequence: 1 givenname: Mohammad A. surname: Eita fullname: Eita, Mohammad A. email: mohammad.eita@ejust.edu.eg organization: Dept. of Comput. Sci. & Eng., Egypt-Japan Univ. of Sci. & Technol., Alex, Egypt – sequence: 2 givenname: Tetsuo surname: Shibuya fullname: Shibuya, Tetsuo email: tshibuya@hgc.jp organization: Human Genome Center, Univ. of Tokyo & CREST, Tokyo, Japan – sequence: 3 givenname: Amin A. surname: Shoukry fullname: Shoukry, Amin A. email: amin.shoukry@ejust.edu.eg organization: Dept. of Comput. Sci. & Eng., Egypt-Japan Univ. of Sci. & Technol., Alex, Egypt |
| BookMark | eNotkE1LxDAQhiOs4Hb1LnjpH-g6SZud5ihldYWCFwXxsqT5WKrdZElSxX9vF3t63uF9GIbJyMJ5Zwi5pbCmFMR9s23WDChfI-MIuLkgGa1QCKwEwwVZUqhFgVi_X5Esxk8AWnEqluSj9Uqm3h1y5V0KfhjOOZhD713Mvc2dGYMcJqQfH75iPsZZjinI3hmdm28_jGnyZfidiuNpTPI8XpNLK4dobmauyNvj9rXZFe3L03Pz0BY94zQVggNUCjoDSjO0eiO07ZBRW9UWFceOQddZzQToyasE1LpUwpZcMtFRtOWK3P3v7Y0x-1Poj9Mh-_kP5R-gL1ZW |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/CEC.2015.7257076 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Statistics Computer Science |
| EISBN | 1479974927 9781479974924 |
| EndPage | 1588 |
| ExternalDocumentID | 7257076 |
| Genre | orig-research |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 5VS 6IE 6IF 6IK 6IL 6IN 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ADZIZ AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO CS3 EBS EJD HZ~ H~9 IEGSK IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RIL RNS TN5 VH1 |
| ID | FETCH-LOGICAL-i251t-95004c0be0cd27fd69dfb721f48f7c57b20bbfd290d04c4908d3c9f35a29b17f3 |
| IEDL.DBID | RIE |
| ISSN | 1089-778X |
| IngestDate | Wed Aug 27 02:44:18 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English Japanese |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i251t-95004c0be0cd27fd69dfb721f48f7c57b20bbfd290d04c4908d3c9f35a29b17f3 |
| PageCount | 8 |
| ParticipantIDs | ieee_primary_7257076 |
| PublicationCentury | 2000 |
| PublicationDate | 20150501 |
| PublicationDateYYYYMMDD | 2015-05-01 |
| PublicationDate_xml | – month: 05 year: 2015 text: 20150501 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | IEEE transactions on evolutionary computation |
| PublicationTitleAbbrev | CEC |
| PublicationYear | 2015 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0014519 |
| Score | 1.9084042 |
| Snippet | Detection of controlling regions/driver nodes of the cortical networks helps the networks dynamics reach a desired state. Controllability of the complex... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1581 |
| SubjectTerms | Complex networks Control theory Controllability Evolutionary computation Sociology Statistics Synchronization |
| Title | Locating controlling regions of neural networks using constrained evolutionary computation |
| URI | https://ieeexplore.ieee.org/document/7257076 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH5sw8O8TLeJv8nBo926NG2a89jwIGMHheFlJC-JCNLKfoH_vUnaTQUvnlralIb3EvKS973vA7hjKeJIxjry6qoRS9BGucU80owzzCiT2qggNsFns3yxEPMG3B9qYYwxAXxmBv425PJ1iVt_VDbkXnKNZ01ocp5VtVqHjIGnSanA9MJFjPlin5KMxXA8GXsMVzqov_8lpBLWkWnnfz04gf53QR6ZH5aaU2iYogudvSIDqSdoF45_0At2oe0jyYqIuQcvj6U_nSteSY1O93XoxOsyuHFHSks8s6V8d5eAC18Tj4gPjddBRsJoYnb1OJWrT4Lh58GvfXieTp7GD1EtrBC9uXBmE4nUTQ2MlYlRU251JrRVbitoWW45plzRWCmrqYi1a-dTgzpBYZNUUqFG3CZn0CrKwpwDiXPJlHAvMkzc3ocqlVErlNTIKKYyvYCet-Lyo-LOWNYGvPz78RW0vaMqQOE1tDarrbmBI9w5Y61ug8O_AJjcrpI |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS8MwFH_MKTgv023itzl4tFuXpk1zHhsT59hhwvAy8imCtLIv8L83Sbup4MVTSxva8F5CXvJ-7_cDuCOxlF0eqsCpqwYkkiZIjUwDRSiRCSZcaeHFJuh4nM5mbFKB-10tjNbag8902936XL7K5dodlXWok1yjyR7sO-WsslprlzNwRCkFnJ7ZmDGdbZOSIev0-j2H4orb5Rd-San4lWRQ_18fjqH1XZKHJrvF5gQqOmtAfavJgMop2oCjHwSDDai5WLKgYm7Cyyh353PZKyrx6a4SHTllBjvyUG6Q47bk7_bikeFL5DDxvvHSC0lohfSmHKl88Ymk_7n3bAueB_1pbxiU0grBmw1oVgGL7eSQodChVJgalTBlhN0MGpIaKmMqcCiEUZiFyrZzyUEVSWaimGMmutREp1DN8kyfAQpTTgSzLxIZ2d0PFiLBhgmuJMEy5vE5NJ0V5x8Fe8a8NODF349v4XA4fRrNRw_jx0uoOacV8MIrqK4Wa30NB3JjDbe48c7_AtvBsds |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=IEEE+transactions+on+evolutionary+computation&rft.atitle=Locating+controlling+regions+of+neural+networks+using+constrained+evolutionary+computation&rft.au=Eita%2C+Mohammad+A.&rft.au=Shibuya%2C+Tetsuo&rft.au=Shoukry%2C+Amin+A.&rft.date=2015-05-01&rft.pub=IEEE&rft.issn=1089-778X&rft.spage=1581&rft.epage=1588&rft_id=info:doi/10.1109%2FCEC.2015.7257076&rft.externalDocID=7257076 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-778X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-778X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-778X&client=summon |