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Hybrid artificial immune system-genetic algorithm optimization based on mathematical test functions

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dc.citedby9
dc.contributor.authorAli M.O.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorChong K.H.en_US
dc.contributor.authorYap D.F.W.en_US
dc.contributor.authorid55470919300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid36994481200en_US
dc.contributor.authorid22952562500en_US
dc.date.accessioned2023-12-28T07:17:54Z
dc.date.available2023-12-28T07:17:54Z
dc.date.issued2010
dc.description.abstractThis paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; firstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efficiency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To differentiate between the results in terms of achieving the minimum value for these functions, eight mathematical test functions are being used to make comparison. �2010 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5704012
dc.identifier.doi10.1109/SCORED.2010.5704012
dc.identifier.epage261
dc.identifier.scopus2-s2.0-79951984379
dc.identifier.spage256
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79951984379&doi=10.1109%2fSCORED.2010.5704012&partnerID=40&md5=3961fa208f9fb007a1cab039ce17815d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29655
dc.pagecount5
dc.sourceScopus
dc.sourcetitleProceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010
dc.subjectArtificial immune system (AIS)
dc.subjectGenetic algorithm (GA) optimization mathematical test functions
dc.subjectHybrid
dc.subjectEngineering research
dc.subjectFunctions
dc.subjectImmunology
dc.subjectInnovation
dc.subjectOptimization
dc.subjectTest facilities
dc.subjectArtificial Immune System
dc.subjectConvergence rates
dc.subjectGenetic-algorithm optimizations
dc.subjectHybrid
dc.subjectHybrid techniques
dc.subjectLocal searching
dc.subjectMinimum value
dc.subjectOptimization method
dc.subjectStand -alone
dc.subjectStandard solutions
dc.subjectTest functions
dc.subjectGenetic algorithms
dc.titleHybrid artificial immune system-genetic algorithm optimization based on mathematical test functionsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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