Genetic algorithm with knowledge-based encoding for interactive fashion design

Hee Su Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Evolutionary computation gives a great potential in several real-world problems as a powerful tool for optimization and classification, but there still remain a lot of obstacles to be applied to artistic domains. To overcome the shortcoming a variety of techniques have been proposed, and among them interactive genetic algorithm (IGA) is extensively studied in these days. IGA exploits the interaction with human in the course of evolution by taking his evaluation as fitness. In this paper, we propose an effective knowledge-based encoding scheme for IGA in a real-world application. This method has been applied to a fashion design aid system, which can reflect user's preference or emotion that is usually difficult to be expressed explicitly. To show that the proposed encoding scheme produces more realistic and practical design, an experimental study as well as a theoretical investigation with schema theorem has been conducted.

Original languageEnglish
Title of host publicationPRICAI 2000, Topics in Artificial Intelligence - 6th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsRiichiro Mizoguchi, John Slaney
PublisherSpringer Verlag
Pages404-414
Number of pages11
ISBN (Print)3540679251, 9783540679257
DOIs
Publication statusPublished - 2000
Event6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, VIC, Australia
Duration: 2000 Aug 282000 Sept 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1886 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000
Country/TerritoryAustralia
CityMelbourne, VIC
Period00/8/2800/9/1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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