An RNA evolutionary algorithm based on gradient descent for function optimization
The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rap...
Uložené v:
| Vydané v: | Journal of computational design and engineering Ročník 11; číslo 4; s. 332 - 357 |
|---|---|
| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Oxford
Oxford University Press
01.08.2024
한국CDE학회 |
| Predmet: | |
| ISSN: | 2288-5048, 2288-4300, 2288-5048 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution’s quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization.
Graphical Abstract
Graphical Abstract |
|---|---|
| AbstractList | The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution’s quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution’s quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. Graphical Abstract Graphical Abstract The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution’s quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. KCI Citation Count: 0 |
| Author | Zhilenkov, Anton A Zhang, Botao Wang, Jian Zhao, Zikai Wu, Qiuxuan Chen, Mingming Chepinskiy, Sergey A Chi, Xiaoni |
| Author_xml | – sequence: 1 givenname: Qiuxuan orcidid: 0000-0001-5153-6524 surname: Wu fullname: Wu, Qiuxuan – sequence: 2 givenname: Zikai surname: Zhao fullname: Zhao, Zikai – sequence: 3 givenname: Mingming surname: Chen fullname: Chen, Mingming – sequence: 4 givenname: Xiaoni surname: Chi fullname: Chi, Xiaoni email: 3794580@qq.com – sequence: 5 givenname: Botao surname: Zhang fullname: Zhang, Botao – sequence: 6 givenname: Jian surname: Wang fullname: Wang, Jian – sequence: 7 givenname: Anton A surname: Zhilenkov fullname: Zhilenkov, Anton A – sequence: 8 givenname: Sergey A surname: Chepinskiy fullname: Chepinskiy, Sergey A |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003114440$$DAccess content in National Research Foundation of Korea (NRF) |
| BookMark | eNp9kF1LwzAUhoNMcM7d-QMCXghi3UnSrtnlGH4MhuKY1yFNk5l9JF3aKvrrbe0uvPLqPQee83J4zlHPeacRuiRwR2DCRhuV69HhU2oY8xPUp5TzKIGY9_7MZ2hYlhsAICllQCZ99Dp1ePk8xfrD7-rKeifDF5a7tQ-2et_jTJY6x97hdZC51a7CuS5Vm8YHbGqn2hvsi8ru7bdslwt0auSu1MNjDtDbw_1q9hQtXh7ns-kiUowmVUQkUSAzDZLFLKdaAk9oariKtaGQcA0sBxVPjOGUkXgMaZJmQMdcxlRniWIDdNP1umDEVlnhpf3NtRfbIKbL1VwQSEnMgTTwVQcXwR9qXVZi4-vgmv8EoxA3HEDaULcdpYIvy6CNKILdN0aaItFKFq1kcZTc4Ncd7uvif_IH89d_Hg |
| Cites_doi | 10.1016/j.eswa.2017.02.012 10.1016/j.cherd.2010.03.005 10.1093/jcde/qwac048 10.1093/jcde/qwad053 10.1093/jcde/qwae004 10.1093/jcde/qwae008 10.1016/j.asoc.2015.09.036 10.1016/j.advengsoft.2016.01.008 10.1093/jcde/qwae046 10.1109/MCI.2006.329691 10.1016/j.compchemeng.2007.01.012 10.1093/jcde/qwac072 10.1016/j.cie.2021.107408 10.1016/j.asoc.2021.108357 10.1007/s11227-022-04959-6 10.1016/S0045-7825(99)00389-8 10.1016/j.advengsoft.2013.12.007 10.1007/s12065-023-00822-6 10.1142/S1469026819500202 10.1016/j.amc.2010.12.053 10.1016/j.ins.2009.03.004 10.1007/s00366-020-00951-x 10.1007/s42979-022-01607-x 10.1016/j.ijhydene.2012.10.026 10.1093/jcde/qwad095 10.1016/j.ins.2023.119164 10.1007/s12559-020-09730-8 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. 2024 The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. 2024 – notice: The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | TOX AAYXX CITATION 7XB 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M0N M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U ACYCR |
| DOI | 10.1093/jcde/qwae068 |
| DatabaseName | Oxford Journals Open Access Collection CrossRef ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central Technology Collection ProQuest One ProQuest Central Korea SciTech Premium Collection ProQuest Engineering Collection Computing Database Engineering Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic Korean Citation Index |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Computing Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Basic ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences |
| EISSN | 2288-5048 |
| EndPage | 357 |
| ExternalDocumentID | oai_kci_go_kr_ARTI_10714801 10_1093_jcde_qwae068 10.1093/jcde/qwae068 |
| GroupedDBID | 0R~ 4.4 457 5VS AAEDT AAEDW AAIKJ AALRI AAPXW AAVAP AAXUO AAYWO ABEJV ABGNP ABJCF ABMAC ABPTD ABXVV ACGFS ACVFH ADBBV ADCNI ADEZE ADMLS ADVLN AEUPX AEXQZ AFKRA AFPUW AFTJW AGHFR AIGII AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMNDL AMRAJ AZQEC BCNDV BENPR BGLVJ CCPQU DWQXO EBS EJD FDB FRF GNUQQ GROUPED_DOAJ H13 HCIFZ IAO IGS IPNFZ ITC JDI KQ8 KSI M41 M7S ML0 M~E O9- OK1 PHGZM PHGZT PIMPY PTHSS RIG ROL SSZ TOX AAYXX AFFHD CITATION PQGLB 7XB 8FE 8FG ABUWG L6V M0N PKEHL PQEST PQQKQ PQUKI PRINS Q9U ACYCR PMFND |
| ID | FETCH-LOGICAL-c325t-1a1c0abe0a343d2ea08527f8c4ef2058e03d0c49ff8231460757b0268a42eb5c3 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001298170100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2288-5048 2288-4300 |
| IngestDate | Thu Jun 12 03:20:27 EDT 2025 Fri Sep 19 20:56:59 EDT 2025 Sat Nov 29 03:52:55 EST 2025 Mon Jun 30 08:34:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | RNA-inspired operations heuristic algorithm function optimization adaptive gradient descent mutation operator |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com https://creativecommons.org/licenses/by-nc/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c325t-1a1c0abe0a343d2ea08527f8c4ef2058e03d0c49ff8231460757b0268a42eb5c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5153-6524 |
| OpenAccessLink | https://www.proquest.com/docview/3204107007?pq-origsite=%requestingapplication% |
| PQID | 3204107007 |
| PQPubID | 7217057 |
| PageCount | 26 |
| ParticipantIDs | nrf_kci_oai_kci_go_kr_ARTI_10714801 proquest_journals_3204107007 crossref_primary_10_1093_jcde_qwae068 oup_primary_10_1093_jcde_qwae068 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-01 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Journal of computational design and engineering |
| PublicationYear | 2024 |
| Publisher | Oxford University Press 한국CDE학회 |
| Publisher_xml | – name: Oxford University Press – name: 한국CDE학회 |
| References | Mirjalili (2024082710544546100_bib31) 2016 Zhang (2024082710544546100_bib56) 2013; 38 Molina (2024082710544546100_bib35) 2020; 12 Chuang (2024082710544546100_bib6) 2016; 38 Vidyashree (2024082710544546100_bib48) 2023; 4 Soares (2024082710544546100_bib42) 2017; 78 Umbarkar (2024082710544546100_bib47) 2015; 6 Haupt (2024082710544546100_bib18) 2004 Qian (2024082710544546100_bib36) 2023 Tieleman (2024082710544546100_bib46) 2012; 4 Wang (2024082710544546100_bib49) 2018; 1 Choze (2024082710544546100_bib5) 2022; 1 El-Mihoub (2024082710544546100_bib14) 2006; 13 Rechenberg (2024082710544546100_bib39) 1978; 8 Abdollahzadeh (2024082710544546100_bib1) 2021; 158 Tian (2024082710544546100_bib45) 2021; 54 Lemaréchal (2024082710544546100_bib24) 2012; 251 Tao (2024082710544546100_bib43) 2007; 31 Wang (2024082710544546100_bib51) 2010; 88 Liu (2024082710544546100_bib27) 2022; 117 Lu (2024082710544546100_bib29) 2024; 11 Sattar (2024082710544546100_bib40) 2021; 37 Deb (2024082710544546100_bib7) 2000; 186 Lameesa (2024082710544546100_bib23) 2024; 11 Liu (2024082710544546100_bib28) 2023; 642 Deb (2024082710544546100_bib8) 1995; 9 Dorigo (2024082710544546100_bib12) 2006; 1 Duchi (2024082710544546100_bib13) 2011; 12 Xue (2024082710544546100_bib54) 2023; 79 Gandomi (2024082710544546100_bib16) 2014; 1 Shukla (2024082710544546100_bib41) 2019; 18 Jia (2024082710544546100_bib20) 2023; 10 Rashedi (2024082710544546100_bib38) 2009; 179 Wang (2024082710544546100_bib52) 2024; 11 Dhiman (2024082710544546100_bib9) 2017 Kim (2024082710544546100_bib21) 2022; 9 Wang (2024082710544546100_bib50) 2019; 1 Mirjalili (2024082710544546100_bib33) 2016; 95 Mirjalili (2024082710544546100_bib34) 2014; 69 Hussein (2024082710544546100_bib19) 2023; 10 Zhu (2024082710544546100_bib57) 2022 Liang (2024082710544546100_bib26) 2006; 41 Wu (2024082710544546100_bib53) 2017 Mirjalili (2024082710544546100_bib32) 2017 Alhijawi (2024082710544546100_bib2) 2023; 17 Domala (2024082710544546100_bib10) 2022; 9 Kingma (2024082710544546100_bib22) 2014 Thangaraj (2024082710544546100_bib44) 2011; 217 |
| References_xml | – volume-title: Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-parameter Optimization year: 2017 ident: 2024082710544546100_bib53 – start-page: 163 volume-title: Advances in Engineering Software year: 2017 ident: 2024082710544546100_bib32 article-title: Salp swarm algorithm: A bio-inspired optimizer for engineering design problems – volume: 78 start-page: 32 year: 2017 ident: 2024082710544546100_bib42 article-title: Optimization based on phylogram analysis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.02.012 – volume: 88 start-page: 1485 year: 2010 ident: 2024082710544546100_bib51 article-title: A novel RNA genetic algorithm for parameter estimation of dynamic systems publication-title: Chemical Engineering Research and Design doi: 10.1016/j.cherd.2010.03.005 – volume: 9 start-page: 1107 year: 2022 ident: 2024082710544546100_bib10 article-title: Wave data prediction with optimized machine learning and deep learning techniques publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwac048 – volume: 10 start-page: 1363 year: 2023 ident: 2024082710544546100_bib19 article-title: Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwad053 – volume: 11 start-page: 37 year: 2024 ident: 2024082710544546100_bib52 article-title: Boosting Aquila optimizer by marine predators algorithm for combinatorial optimization publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwae004 – volume: 11 start-page: 212 year: 2024 ident: 2024082710544546100_bib29 article-title: Conceptual design and optimization of polymer gear system for low-thrust turbofan aeroengine accessory transmission publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwae008 – volume: 54 start-page: 1 year: 2021 ident: 2024082710544546100_bib45 article-title: Evolutionary large-scale multi-objective optimization: a survey publication-title: ACM Computing Surveys (CSUR) – volume: 12 start-page: 2121−2159 year: 2011 ident: 2024082710544546100_bib13 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: Journal of Machine Learning Research – volume: 38 start-page: 87 year: 2016 ident: 2024082710544546100_bib6 article-title: A simple and efficient real-coded genetic algorithm for constrained optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.09.036 – volume: 95 start-page: 51 year: 2016 ident: 2024082710544546100_bib33 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – start-page: 120 volume-title: Knowledge-based Systems year: 2016 ident: 2024082710544546100_bib31 article-title: SCA: A sine cosine algorithm for solving optimization problems – volume: 11 start-page: 223 year: 2024 ident: 2024082710544546100_bib23 article-title: Role of metaheuristic algorithms in healthcare: a comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwae046 – start-page: 48 volume-title: Advances in Engineering Software year: 2017 ident: 2024082710544546100_bib9 article-title: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications – volume: 13 start-page: 124 year: 2006 ident: 2024082710544546100_bib14 article-title: Hybrid genetic algorithms: A publication-title: Review. Engineering Letters – volume: 1 start-page: 28 year: 2006 ident: 2024082710544546100_bib12 article-title: Ant colony optimization publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2006.329691 – volume: 31 start-page: 1602 year: 2007 ident: 2024082710544546100_bib43 article-title: DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes publication-title: Computers & Chemical Engineering doi: 10.1016/j.compchemeng.2007.01.012 – volume: 6 start-page: 2121 year: 2015 ident: 2024082710544546100_bib47 article-title: Crossover operators in genetic algorithms: a review publication-title: ICTACT Journal on Soft Computing – volume-title: Practical Genetic Algorithms year: 2004 ident: 2024082710544546100_bib18 – volume: 9 start-page: 1650 year: 2022 ident: 2024082710544546100_bib21 article-title: Computed tomography vertebral segmentation from multi-vendor scanner data publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwac072 – volume: 158 start-page: 107408 year: 2021 ident: 2024082710544546100_bib1 article-title: African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107408 – volume: 8 start-page: 973 year: 1978 ident: 2024082710544546100_bib39 article-title: Evolutionsstrategien publication-title: Simulationsmethoden in der Medizin und Biologie – volume: 1 start-page: 107 year: 2022 ident: 2024082710544546100_bib5 article-title: Overview of traditional and recent heuristic optimization methods. In model-based and signal-based inverse methods publication-title: Biblioteca Central Da Universidade De Brasilia – start-page: 101647 volume-title: Urban Climate year: 2023 ident: 2024082710544546100_bib36 article-title: Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption[J] – volume: 117 start-page: 108357 year: 2022 ident: 2024082710544546100_bib27 article-title: A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2021.108357 – start-page: 108326 volume-title: Mechanical Systems and Signal Processing year: 2022 ident: 2024082710544546100_bib57 article-title: Hairpin RNA genetic algorithm based ANFIS for modeling overhead cranes – volume: 79 start-page: 7305 year: 2023 ident: 2024082710544546100_bib54 article-title: Dung beetle optimizer: a new meta-heuristic algorithm for global optimization publication-title: The Journal of Supercomputing doi: 10.1007/s11227-022-04959-6 – volume: 186 start-page: 311 year: 2000 ident: 2024082710544546100_bib7 article-title: An efficient constraint handling method for genetic algorithms publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/S0045-7825(99)00389-8 – volume: 251 start-page: 10 year: 2012 ident: 2024082710544546100_bib24 article-title: Cauchy and the gradient method publication-title: Doc Math Extra – volume: 4 start-page: 26 year: 2012 ident: 2024082710544546100_bib46 article-title: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude[J] publication-title: COURSERA: Neural Networks for Machine Learning – volume: 1 start-page: 1 year: 2014 ident: 2024082710544546100_bib16 article-title: Engineering optimization using interior search algorithm publication-title: 2014 IEEE Symposium on Swarm Intelligence – volume: 69 start-page: 46 year: 2014 ident: 2024082710544546100_bib34 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 17 start-page: 1245 year: 2023 ident: 2024082710544546100_bib2 article-title: Genetic algorithms: theory, genetic operators, solutions, and applications publication-title: Evolutionary Intelligence doi: 10.1007/s12065-023-00822-6 – volume: 41 start-page: 8 year: 2006 ident: 2024082710544546100_bib26 article-title: Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization publication-title: Journal of Applied Mechanics – volume: 9 start-page: 115 year: 1995 ident: 2024082710544546100_bib8 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume: 1 start-page: 1959 year: 2019 ident: 2024082710544546100_bib50 article-title: An improved real-coded genetic algorithm using the heuristical normal distribution and direction-based crossover publication-title: Computational Intelligence and Neuroscience – volume: 18 start-page: 1950020 year: 2019 ident: 2024082710544546100_bib41 article-title: A new hybrid feature subset selection framework based on binary genetic algorithm and information theory publication-title: International Journal of Computational Intelligence and Applications doi: 10.1142/S1469026819500202 – volume: 217 start-page: 5208 year: 2011 ident: 2024082710544546100_bib44 article-title: Particle swarm optimization: hybridization perspectives and experimental illustrations publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2010.12.053 – volume: 179 start-page: 2232 year: 2009 ident: 2024082710544546100_bib38 article-title: GSA: a gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – volume: 37 start-page: 2389 year: 2021 ident: 2024082710544546100_bib40 article-title: A smart metaheuristic algorithm for solving engineering problems publication-title: Engineering with Computers doi: 10.1007/s00366-020-00951-x – volume: 4 start-page: 190 year: 2023 ident: 2024082710544546100_bib48 article-title: An improvised sentiment analysis model on Twitter data using stochastic gradient descent (SGD) optimization algorithm in stochastic gate neural network (SGNN) publication-title: SN Computer Science doi: 10.1007/s42979-022-01607-x – volume: 1 start-page: 1 year: 2018 ident: 2024082710544546100_bib49 article-title: Improvement analysis and application of real-coded genetic algorithm for solving constrained optimization problems publication-title: Mathematical Problems in Engineering – volume: 38 start-page: 219 year: 2013 ident: 2024082710544546100_bib56 article-title: An adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cells publication-title: International Journal of Hydrogen Energy doi: 10.1016/j.ijhydene.2012.10.026 – volume: 10 start-page: 2223 year: 2023 ident: 2024082710544546100_bib20 article-title: Improve coati optimization algorithm for solving constrained engineering optimization problems publication-title: Journal of Computational Design and Engineering doi: 10.1093/jcde/qwad095 – volume-title: Proceedings of the 3rd International Conference on Learning Representations (ICLR) year: 2014 ident: 2024082710544546100_bib22 article-title: Adam: a method for stochastic optimization – volume: 642 start-page: 119164 year: 2023 ident: 2024082710544546100_bib28 article-title: A late-mover genetic algorithm for resource-constrained project-scheduling problems publication-title: Information Sciences doi: 10.1016/j.ins.2023.119164 – volume: 12 start-page: 897 year: 2020 ident: 2024082710544546100_bib35 article-title: Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations publication-title: Cognitive Computation doi: 10.1007/s12559-020-09730-8 |
| SSID | ssj0001723019 ssib053376903 |
| Score | 2.2706609 |
| Snippet | The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control... |
| SourceID | nrf proquest crossref oup |
| SourceType | Open Website Aggregation Database Index Database Publisher |
| StartPage | 332 |
| SubjectTerms | Adaptive algorithms Dung Evolutionary algorithms Genetic algorithms Heuristic methods Independent variables Mutation Operators (mathematics) Optimization Optimization algorithms Process controls Ribonucleic acid RNA 기계공학 |
| Title | An RNA evolutionary algorithm based on gradient descent for function optimization |
| URI | https://www.proquest.com/docview/3204107007 https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART003114440 |
| Volume | 11 |
| WOSCitedRecordID | wos001298170100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | Journal of Computational Design and Engineering , 2024, 11(4), , pp.332-357 |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: TOX dateStart: 20140101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: M7S dateStart: 20211001 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: BENPR dateStart: 20211001 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2288-5048 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001723019 issn: 2288-5048 databaseCode: PIMPY dateStart: 20211001 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT-MwEB7xOnChy0tbYCtLwDGq4zhNckJlBVoOlPKSyslyHLuwQELTAOLfM05cseKwHDhFeVlRZjzzzXj8DcCe7qEXNRHzUkQHHjc88WQvMh4ifV9mhjLlm7rZRDQYxKNRMnQJt6krq5zZxNpQZ4WyOfJuwCjHUAVd2sHTxLNdo-zqqmuhMQ-LliXBr0v3Lj9yLBEC7Lq3B2OoECFqq6t9xzi--1dlujt5lZpantV_vNJ8XppP-91mBrr2Oset737vD1hxeJP0GwVZhTmdr0HLYU_iZvZ0Hc77ObkY9Il-cbooyzciH8Y4ZHX7SKyzy0iRk3FZ14hVJGt4oAiCXmKdo32HFGiAHt3Ozg24Pj66-v3Hc-0WPBWwsPJ86SsqU01lwIOMaYlojEUmVlwbRsNY0yCjiifG2KVD3kOwEaUYwsWSM52GKtiEhbzI9U8gCEtYZnv9MR3yQCcx5akJDUasCYuk0W3Yn_1u8dSwaohmNTwQVizCiaUNuygLca_uhKXBtsdxIe5LgWD_BB-PfMt-0waCsvpioJ2ZlISbpFPxIaKt_9_ehmWGWKap-9uBhap81r9gSb1Ud9OyA4uHR4PhRacO5zu1BuK14cnp8AbPrs5G7_zj5CA |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB61BQkulKdYKGAJeozWGTub5IDQCqi6alnxKFJvxnHspa-kzYZW_VP8RsaJoyIOcOqBUw6JLTn-PN839ngG4JWdEIu6FKOC1EEkncwjPUldREo_1qXjaGLXFZtI5_Nsfz__uAI_h7swPqxysImdoS5r4_fIxwK5JFeFKO3N6Vnkq0b509WhhEYPix17eUEu2_L17B3N7ybi1vu9t9tRqCoQGYFJG8U6NlwXlmshRYlWk-jA1GVGWoc8ySwXJTcyd86fkMkJcWpakKeSaYm2SIygflfhBskIzLtQwS9XezopCfqulggiATCh1RFi7XkuxoemtOOzC225z-v6GwuuVo37437dQAgdy22t_2__5y7cCXqaTfsFcA9WbHUf1oO2ZsFyLR_Ap2nFPs-nzJ6HtaabS6aPFzSE9vsJ82Resrpii6aLgWtZ2ee5YiTqmSd_34bVZGBPws3Vh_D1Wgb2CNaqurKPgZHswtLXMkSbSGHzjMvCJY488hxT7ewINofpVad91hDVn_YL5WGgAgxG8JLmXh2ZA-XTfPvnolZHjSJnZkafp7HP7jMCRtj4R0cbAypUMEJLdQWJJ39__QJube992FW7s_nOU7iNpNv6GMcNWGubH_YZ3DTn7cGyed7hncG36wbQLwltOn8 |
| 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=article&rft.atitle=An+RNA+evolutionary+algorithm+based+on+gradient+descent+for+function+optimization&rft.jtitle=Journal+of+computational+design+and+engineering&rft.au=Wu%2C+Qiuxuan&rft.au=Zhao%2C+Zikai&rft.au=Chen%2C+Mingming&rft.au=Chi%2C+Xiaoni&rft.date=2024-08-01&rft.pub=Oxford+University+Press&rft.issn=2288-5048&rft.eissn=2288-5048&rft.volume=11&rft.issue=4&rft.spage=332&rft.epage=357&rft_id=info:doi/10.1093%2Fjcde%2Fqwae068 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2288-5048&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2288-5048&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2288-5048&client=summon |