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Cited by 435

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Artificial evolution for computer graphics

Open access
Sims, Karl·1991·ACM SIGGRAPH Computer Graphics

Abstract

This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to direct simulated evolutions in preferred directions. Several examples using these methods have been implemented and are described. 3D plant structures are grown using fixed sets of genetic parameters. Images, solid textures, and animations are created using mutating symbolic lisp expressions. Genotypes consisting of symbolic expressions are presented as an attempt to surpass the limitations of fixed-length genotypes with predefined expression rules. It is proposed that artificial evolution has potential as a powerful tool for achieving flexible complexity with a minimum of user input and knowledge of details.

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Metadata

Authors
Sims, K.
Venue
ACM SIGGRAPH Computer Graphics
Published
1991-07
Volume
25
Issue
4
Pages
319-328
ISSN
0097-8930 (ISSN-L)
OpenAlex
W2996879375
Cited by
435
References
13
FWCI
26.50
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Artificial evolution for computer graphics. https://app.cochat.ai/papers/10.1145/127719.122752
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Sims, K. (1991). Artificial evolution for computer graphics. ACM SIGGRAPH Computer Graphics. https://doi.org/10.1145/127719.122752

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.