Genetic learning for biologically inspired aesthetic processes
Inhaltsverzeichnis
Reference
Greenfield, G.: Genetic learning for biologically inspired aesthetic processes. International Journal on Artificial Intelligence Tools 15(4), 577–598 (2006)
DOI
http://dx.doi.org/10.1142/S0218213006002825
Abstract
We investigate the use of the non-interactive genetic algorithm as a tool in evolutionary art for evolving aesthetic images. We consider two problem domains. Our first uses a modification of a model for differential gene expression in order to simulate cell morphology and evolve images consisting of matrices of cells meeting our subjective aesthetic criteria. Our second uses a modification of a model for simulating ants that can deposit and follow scent (in the guise of color trails) in order to evolve ant paintings meeting out subjective aesthetic criteria. In both cases, we focus upon the design of fitness functions that are particularly well suited for ensuring that genetic learning can effectively guide the evolution of cellular or behavioral processes to yield aesthetic results.
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