Evolutionary Learning of Graph Layout Constraints from Examples

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Masui94,

• author = "Toshiyuki Masui",
• title = "Evolutionary Learning of Graph Layout Constraints from Examples",
• booktitle = "Proceedings of the ACM Symposium on User Interface Software and Technology",
• series = "Demonstrational User Interfaces",
• pages = "103--108",
• year = "1994",
• copyright = "(c) Copyright 1994 Association for Computing Machinery",
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Graphic object layout, Graph layout, Programming by example, Adaptive user interface",
• URL = "http://delivery.acm.org/10.1145/200000/192468/p103-masui.pdf?key1=192468&key2=4873616011&coll=GUIDE&dl=GUIDE&CFID=36810799&CFTOKEN=40084029",
• URL = "http://www.acm.org/pubs/articles/proceedings/uist/192426/p103-masui/p103-masui.pdf",
• DOI = "doi:10.1145/192426.192468",
• size = "6 pages",
• 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.",
• notes = "MRnumber = C.UIST.94.103",

}

Genetic Programming entries for Toshiyuki Masui

Citations