Evolutionary learning of graph layout constraints from examples

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

20 Citations (Scopus)

Abstract

We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.

Original languageEnglish
Title of host publicationProceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994
PublisherAssociation for Computing Machinery, Inc
Pages103-108
Number of pages6
ISBN (Electronic)0897916573, 9780897916578
DOIs
Publication statusPublished - 1994 Nov 2
Externally publishedYes
Event7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994 - Marina del Rey, United States
Duration: 1994 Nov 21994 Nov 4

Other

Other7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994
CountryUnited States
CityMarina del Rey
Period94/11/294/11/4

Fingerprint

Function evaluation
Genetic programming
Simulated annealing
User interfaces
Genetic algorithms

Keywords

  • Adaptive user interface
  • Genetic algorithms
  • Genetic programming
  • Graph layout
  • Graphic object layout
  • Programming by example

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Masui, T. (1994). Evolutionary learning of graph layout constraints from examples. In Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994 (pp. 103-108). Association for Computing Machinery, Inc. https://doi.org/10.1145/192426.192468

Evolutionary learning of graph layout constraints from examples. / Masui, Toshiyuki.

Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994. Association for Computing Machinery, Inc, 1994. p. 103-108.

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

Masui, T 1994, Evolutionary learning of graph layout constraints from examples. in Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994. Association for Computing Machinery, Inc, pp. 103-108, 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994, Marina del Rey, United States, 94/11/2. https://doi.org/10.1145/192426.192468
Masui T. Evolutionary learning of graph layout constraints from examples. In Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994. Association for Computing Machinery, Inc. 1994. p. 103-108 https://doi.org/10.1145/192426.192468
Masui, Toshiyuki. / Evolutionary learning of graph layout constraints from examples. Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994. Association for Computing Machinery, Inc, 1994. pp. 103-108
@inproceedings{710425efe7644d879e85083e952c748a,
title = "Evolutionary learning of graph layout constraints from examples",
abstract = "We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.",
keywords = "Adaptive user interface, Genetic algorithms, Genetic programming, Graph layout, Graphic object layout, Programming by example",
author = "Toshiyuki Masui",
year = "1994",
month = "11",
day = "2",
doi = "10.1145/192426.192468",
language = "English",
pages = "103--108",
booktitle = "Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994",
publisher = "Association for Computing Machinery, Inc",

}

TY - GEN

T1 - Evolutionary learning of graph layout constraints from examples

AU - Masui, Toshiyuki

PY - 1994/11/2

Y1 - 1994/11/2

N2 - We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.

AB - We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing and genetic algorithms, automatic layout systems can find a good layout using an evaluation function which can calculate how good a given layout is. However, the evaluation function is usually not known beforehand, and it might vary from user to user. In our system, users show the system several pairs of good and bad layout examples, and the system infers the evaluation function from the examples using genetic programming technique. After the evaluation function evolves to reflect the preferences of the user, it is used as a general evaluation function for laying out graphs. The same technique can be used for a wide range of adaptive user interface systems.

KW - Adaptive user interface

KW - Genetic algorithms

KW - Genetic programming

KW - Graph layout

KW - Graphic object layout

KW - Programming by example

UR - http://www.scopus.com/inward/record.url?scp=84877018331&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84877018331&partnerID=8YFLogxK

U2 - 10.1145/192426.192468

DO - 10.1145/192426.192468

M3 - Conference contribution

AN - SCOPUS:84877018331

SP - 103

EP - 108

BT - Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology, UIST 1994

PB - Association for Computing Machinery, Inc

ER -