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Towards Automated Artificial Evolution for Computer-generated Images

 

作者: SHUMEET BALUJA,   DEAN POMERLEAU,   TODD JOCHEM,  

 

期刊: Connection Science  (Taylor Available online 1994)
卷期: Volume 6, issue 2-3  

页码: 325-354

 

ISSN:0954-0091

 

年代: 1994

 

DOI:10.1080/09540099408915729

 

出版商: Taylor & Francis Group

 

关键词: Artificial neural networks;computer-generated images;genetic algorithms;genetic programming

 

数据来源: Taylor

 

摘要:

In 1991, Karl Sims presented work on artificial evolution in which he used genetic algorithms to evolve complex structures for use in computer-generated images and animations. The evolution of the computer-generated images progressed from simple, randomly generated shapes to interesting images which the users created interactively. The evolution advanced under the constant guidance and supervision of the user. This paper describes attempts to automate the process of image evolution through the use of artificial neural networks. The central objective of this study is to learn the user's preferences, and to apply this knowledge to evolve aesthetically pleasing images which are similar to those evolved through interactive sessions with the user. This paper presents a detailed performance analysis of both the successes and shortcomings encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.

 

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