OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms

Viktor Schmuck, David Meredith

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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Abstract

We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II
EditorsPeggy Cellier, Kurt Driessens
Number of pages8
Volume1168
Place of PublicationCham, Switzerland
PublisherSpringer
Publication date2020
Pages509-516
ISBN (Print)978-3-030-43886-9
ISBN (Electronic)978-3-030-43887-6
DOIs
Publication statusPublished - 2020
EventInternational Workshop on Machine Learning and Music - Würzburg, Germany
Duration: 16 Sep 201916 Sep 2019
Conference number: 12
https://musml2019.weebly.com/

Conference

ConferenceInternational Workshop on Machine Learning and Music
Number12
Country/TerritoryGermany
CityWürzburg
Period16/09/201916/09/2019
Internet address
SeriesCommunications in Computer and Information Science
Volume1168
ISSN1865-0929

Keywords

  • COSIATEC
  • Forth’s algorithm
  • Genetic algorithm
  • Geometric algorithms
  • Music analysis
  • OMNISIA
  • Parameter optimization
  • Pattern discovery
  • Point sets

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