The artwork is highly interdisciplinary (genetic programming, cognitive creativity theory, evolutionary art, new media portraiture) and has both aesthetic and scientific goals. This section speaks to the research side of the project. Most creative evolutionary systems, use a human (often the artist or viewer under interactive control) to make the aesthetic decisions after each population. In contrast, we are specifically researching creative evolutionary systems that use an automatic fitness function where the computer judges aesthetic or creativity fitness, which is a more open-ended research problem.

Our main research direction is to explore computer creativity modeling as a technique to better understand how humanís experience creativity. Unlike typical Genetic Programming systems this system favors exploration over optimization, finding innovative or novel solutions over a preconceived notion of a specific optimal solution. Our work is based on new discoveries in human creativity, especially in terms of fluid contextual focus, which has been implemented into this evolutionary software. Human creativity is not just a matter of eliminating rules but assimilating and then breaking free of them where warranted. Indeed a considerable body of research suggests that the creative process involves not just increased fluidity or free associative thought, but increased fluidity tempered with increased restraint. From this human creativity research we have created a computer fitness function that is more fluid between being tightly focuses on resemblance (similarity to the sitter image, which in this case is the Darwin portrait) or can swing (based on functional triggers) towards a more open associative process around intertwining and at times contradicting 'rules' of abstract portrait painting, in this case: 1) weighing for face versus background composition, 2) weighing tonal similarity over exact color similarity matched with an artistic color space model and 3) unequal dominat and subdominant tone and color rules based on a portrait painter knowledge domain represented by figure 1. For additional conceptual details, please review the published papers below.


Figure 1. Our 2nd generation fitness function mimics human creativity by moving between restrained focus (resemblance) to more unstructured associative focus (resemblance + more ambiguous art rules of composition, tonality and color theory)

Figure 2. Two portrait programs are mated together showing merged strategies of the offspring. Since the genes of each portrait can be saved, it is possible to re-combine (marry) and re-evolve any of the art works in new variants (Figure 2).

Video Excerpts (youtube) of concepts from MIT and Cambridge Shows.


[PDF] DiPaola S, Gabora L, "Incorporating Characteristics of Human Creativity into an Evolutionary Art Algorithm" Genetic Programming and Evolvable Machines Journal, in press, 2009.

[PDF] DiPaola S, Gabora L, "Incorporating Characteristics of Human Creativity into an Evolutionary Art Algorithm", In Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation (London,, July 07 - 11, 2007). GECCO '07. ACM, New York, NY, 2450-2456.

[PDF] DiPaola S, "Evolving Creative Portrait Painter Programs Using Darwinian Techniques with an Automatic Fitness Function", Proceedings of Electronic Imaging & Visual Arts, London. July. 2005.


Related Research Papers:

[PDF] DiPaola S, "Exploring a Parameterized Portrait Painting Space", International Journal of Art and Technology, in press, 2009.

See DiPaola's research lab's site iVizLab for additional information.