Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2007.912741
DC FieldValue
dc.titleContext-dependent DNA coding with redundancy and introns
dc.contributor.authorXiao, P.
dc.contributor.authorVadakkepat, P.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T02:42:44Z
dc.date.available2014-06-17T02:42:44Z
dc.date.issued2008-04
dc.identifier.citationXiao, P., Vadakkepat, P., Lee, T.H. (2008-04). Context-dependent DNA coding with redundancy and introns. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38 (2) : 331-341. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2007.912741
dc.identifier.issn10834419
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/55408
dc.description.abstractDeoxyribonucleic acid (DNA) coding methods determine the meaning of a certain character in individual chromosomes by the characters surrounding it. The meaning of each character is context dependent, not position dependent. Although position-dependent coding is most commonly used in genetic algorithms (GAs), a context-dependent coding formation is in fact more closer to the natural DNA chromosome. With the context dependency, the DNA coding methods allow intron parts, redundancy, and variable string length in encoded strings while remaining compatible with the standard genetic operations. This paper tries to explicitly explore the influence of those special features of the DNA coding scheme. Two fundamental DNA coding methods (with and without the use of introns) are constructed and compared with the integer coding method, which lacks the features of interest. The performance of the proposed DNA coding methods is analyzed through the robot soccer role assignment problem. The context-dependent coding exhibits the advantages in handling the negative effect of epistasis. The redundancy and intron parts are helpful in preventing useful schemata from disruption and in increasing the population diversity. The variable length of the individual string enables GAs to evolve both the size and the structure of the fuzzy rule base. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSMCB.2007.912741
dc.sourceScopus
dc.subjectBehavior-based architecture
dc.subjectDeoxyribonucleic acid (DNA) coding
dc.subjectFuzzy control
dc.subjectGenetic algorithm (GA)
dc.subjectRobotic soccer
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TSMCB.2007.912741
dc.description.sourcetitleIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
dc.description.volume38
dc.description.issue2
dc.description.page331-341
dc.description.codenITSCF
dc.identifier.isiut000254029400006
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