%0 Journal Article %1 baluja:1994:taaecgi %A Baluja, Shumeet %A Pomerleau, Dean %A Jochem, Todd %D 1994 %J Connection Science %K (ANN), algorithms, artificial computer evolution, genetic graphics networks neural programming, simulated %N 2 and 3 %P 325--354 %T Towards Automated Artificial Evolution for Computer-generated Images %U http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf %V 6 %X 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 interactively created. 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 analysis of both the shortcomings and successes encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.

@article{baluja:1994:taaecgi, abstract = { 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 interactively created. 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 analysis of both the shortcomings and successes encountered in the use of five artificial neural network architectures. Further possibilities for improving the performance of a fully automated system are also discussed.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Baluja, Shumeet and Pomerleau, Dean and Jochem, Todd}, biburl = {https://www.bibsonomy.org/bibtex/2a82d8c3252a04f881b9d77ccfc4f9861/brazovayeye}, interhash = {62acb2ce1898bb2e4f1b936fadbdc535}, intrahash = {a82d8c3252a04f881b9d77ccfc4f9861}, journal = {Connection Science}, keywords = {(ANN), algorithms, artificial computer evolution, genetic graphics networks neural programming, simulated}, notes = {also CMU techical report CMU//CS-93-198}, number = {2 and 3}, pages = {325--354}, size = {30 pages}, timestamp = {2008-06-19T17:36:11.000+0200}, title = {Towards Automated Artificial Evolution for Computer-generated Images}, url = {http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf}, volume = 6, year = 1994 }