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dc.contributor.authorZhao, Kaien
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorWang, Dingweien
dc.date.accessioned2017-03-22T11:52:39Z
dc.date.available2017-03-22T11:52:39Z
dc.date.issued1998
dc.identifier.citationZhao, K., Yang, S. and Wang, D. (1998) Genetic algorithm and neural network hybrid approach for job-shop scheduling. Proceedings of the IASTED Int. Conf. on Applied Modelling and Simulation (AMS'98), pp. 110-114en
dc.identifier.urihttp://hdl.handle.net/2086/13819
dc.description.abstractThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.en
dc.language.isoen_USen
dc.publisherACTA Pressen
dc.subjectJob-shop schedulingen
dc.subjectGenetic algorithmen
dc.subjectNeural networken
dc.subjectConstraint satisfactionen
dc.titleGenetic algorithm and neural network hybrid approach for job-shop schedulingen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Nature Science Foundation of Chinaen
dc.funderNational High-Tech Program of Chinaen
dc.projectid69684005en
dc.projectid863-511-9609-003en
dc.cclicenceCC-BY-NC-NDen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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