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dc.contributor.authorKang, Po-Yaen_US
dc.contributor.authorWu, I-Chenen_US
dc.contributor.authorHsueh, Chu-Hsuanen_US
dc.date.accessioned2017-04-21T06:48:42Z-
dc.date.available2017-04-21T06:48:42Z-
dc.date.issued2015en_US
dc.identifier.isbn978-1-4673-9606-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/135994-
dc.description.abstractIn this paper, we solve the portfolio selection problem. In our approach, we first propose a modified immune algorithm (IA) to reuse the memory cells we got in earlier stages, so that more information can be utilized in the next stages. Our experimental results show that the modified IA can successfully obtain significantly higher return than genetic algorithm (GA) and particle swarm optimization (PSO). Second, we. also propose a hybrid of IA and PSO (IA-PSO), and a hybrid of GA and PSO. From our experiments, the hybrid IA-PSO maintains the high return while becoming more stable.en_US
dc.language.isoen_USen_US
dc.subjectportfolio selection problemen_US
dc.subjectimmune algorithmen_US
dc.subjectgenetic algorithmen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectbootstrappingen_US
dc.titleApplying Hueristic Algorithms to Portfolio Selection Problemen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2015 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI)en_US
dc.citation.spage323en_US
dc.citation.epage329en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000380406200042en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper