Applying hueristic algorithms to portfolio selection problem

Po Ya Kang, I-Chen Wu, Chu Hsuan Hsueh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In 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.

Original languageEnglish
Title of host publicationTAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages323-329
Number of pages7
ISBN (Electronic)9781467396066
DOIs
StatePublished - 12 Feb 2016
EventConference on Technologies and Applications of Artificial Intelligence, TAAI 2015 - Tainan, Taiwan
Duration: 20 Nov 201522 Nov 2015

Publication series

NameTAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence

Conference

ConferenceConference on Technologies and Applications of Artificial Intelligence, TAAI 2015
CountryTaiwan
CityTainan
Period20/11/1522/11/15

Keywords

  • bootstrapping
  • genetic algorithm
  • immune algorithm
  • particle swarm optimization
  • portfolio selection problem

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