Since the pioneer observations of Alan Turing, emotional and aesthetical capabilities have been considered as one of the fundamental element of a genuinely intelligent machine. Among the proposed approaches, genetic algorithms try to combine intuitively a generative impulse with a critical capacity that steers the production towards a valuable goal. The approach here presented is based on Karl Sim's approach in which a set of possible primitives is defined and it represent the genotype of the system. Such expressions are combined using genetic algorithms rules to obtain more complex functions that describe new images. At each step, images are evaluated by the user and this implicitly drives the evolution process. Results can be impressive, however a clear understanding of the determinants of our aesthetic evaluation is presently beyond reach.

Genetic art in perspective / R. Bellini, N.A. Borghese - In: Recent advances of neural network models and applications : proceedings of the 23rd workshop of the Italian neural networks society (SIREN) : may 23-25, Vietri sul Mare, Salerno, Italy / [a cura di] S. Bassis, A. Esposito, F.C. Morabito. - Cham : Springer, 2014. - ISBN 9783319041285. - pp. 87-95 (( Intervento presentato al 23. convegno Workshop of the Italian Neural Networks Society (WIRN) tenutosi a Vietri sul Mare, Salerno nel 2013 [10.1007/978-3-319-04129-2_9].

Genetic art in perspective

N.A. Borghese
Ultimo
2014

Abstract

Since the pioneer observations of Alan Turing, emotional and aesthetical capabilities have been considered as one of the fundamental element of a genuinely intelligent machine. Among the proposed approaches, genetic algorithms try to combine intuitively a generative impulse with a critical capacity that steers the production towards a valuable goal. The approach here presented is based on Karl Sim's approach in which a set of possible primitives is defined and it represent the genotype of the system. Such expressions are combined using genetic algorithms rules to obtain more complex functions that describe new images. At each step, images are evaluated by the user and this implicitly drives the evolution process. Results can be impressive, however a clear understanding of the determinants of our aesthetic evaluation is presently beyond reach.
Settore INF/01 - Informatica
2014
International Neural Network Society (INNS)
European Neural Network Society (ENNS)
IEEE Computational Intelligence Society (CIS)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/236694
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