Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2947688.2947690acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
abstract

Searching for the interesting stuff in a multi-dimensional parameter space

Published: 23 July 2016 Publication History

Abstract

This talk describes work that I have been doing using generative systems and the problems this raises with how to deal with multi-dimensional parameter spaces. In particular I am interested in dealing with problems where there are too many parameters to do a simple exhaustive search, only a small number of parameter combinations are likely to achieve interesting results, but the user still wants to retain creative influence.
For a number of years I have been exploring how intricate complex structures may be created by simulating growth processes. In early work, such the Aggregation (Lomas 2005) and Flow series, a small number of parameters controlled various effects that could bias the growth. These could be explored by simply varying all the parameters independently and running simulations to test the results.
Simple methods such as these work well when there are up to 3 parameters. However, as the number of parameters increase, the task rapidly becomes increasingly complex, and methods that exhaustively sample all the parameters independently are no longer viable.
In this talk I will discuss how I have approached this problem for my recent Cellular Forms (Lomas 2014) and Hybrid Forms (Lomas 2015) works which can have more than 30 parameters, any of which could affect the simulation process in complex and unexpected ways.
In particular, systems that have the potential for interesting emergent results often exhibit difficult behavior, where most sets of parameter values create uninteresting regularity or chaos. Only at the transition areas between these states are the most interesting complex results found.
To help solve these problems I have been developing a tool called 'Species Explorer'. This uses a hybrid approach that combines both evolutionary and lazy machine learning techniques to assist the user find combinations of parameters that may be worth sampling, helping them to explore for novelty as well as to refine particularly promising results.

References

[1]
Lomas, A., 2005. Growth by Aggregation. https://vimeo.com/83297099.
[2]
Lomas, A., 2014. Cellular Forms. https://vimeo.com/160595256.
[3]
Lomas, A., 2015. Hybrid Forms: New Growth. https://vimeo.com/160595256.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DigiPro '16: Proceedings of the 2016 Symposium on Digital Production
July 2016
70 pages
ISBN:9781450344296
DOI:10.1145/2947688
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2016

Check for updates

Author Tags

  1. computationally assisted design
  2. evolutionary design
  3. generative art
  4. machine learning

Qualifiers

  • Abstract

Conference

DigiPro '16
Sponsor:
DigiPro '16: The Digital Production Symposium
July 23, 2016
California, Anaheim

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 200
    Total Downloads
  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media