Objet Trouvé, Holism, and Morphogenesis in Interactive Evolution

Objet Trouve, Holism, and Morphogenesis in Interactive Evolution Ron W. Noel (noelr1@rcn.com) WCSU, Department of Psychology, 181 White Street Danbury, CT 06810 Sylvia Acchione-Noel ( sylvia.acchione-noel@corporate.ge.com ) General Electric, JF Welch Leadership Development Center, Old Albany Post Road Ossining, NY 10562 Abstract Evolutionary systems are conceptualized as having four transfer functions between the two state spaces of genotype and phenotype. The four transfer functions are epigenesis, survival, mate selection, and genetic recombination. The treatment of these transfer functions is uneven at best. In particular, some complain that epigenesis, the formation of an entity from the original undifferentiated cell mass into tissues, is treated in a too simplistic manner to allow for system flexibility, or creativity. This paper reports on an interactive image evolving system that mimics the morphogenesis processes in epigenesis. System description, results, and theoretical implications are discussed. Introduction Interactive evolutionary systems seek to interface evolutionary programming to human preference in order to create systems capable of evolving artifacts that require a human expertise that hasn’t yet succumb to computation. A common area for this endeavor is the evolving of art, particularly image. The interfacing of human ability with machine computation requires resolving difficult issues in the arts, humanities, and sciences. Further, progress in the design of interactive evolutionary systems allows a glimpse into how very human abilities such as intuition, projection, and holistic perception interplay with the mechanics of machine computation. This paper reports on one such interactive evolutionary system that seeks to combine human perception with the genetic algorithm to evolve small holistic images. Humans lack the tremendous numerical computational speed of computers; yet they can process information holistically in an automatic, rapid, and natural manner. Machines possess tremendous computational capabilities; yet no algorithm exists to perform holistic processes as well as humans do. Ideally, a good interactive system would integrate the best human cognitive qualities with machine computational capabilities enabling the resultant hybrid system to outperform either of the two components alone. Evolutionary computation as an algorithm is well suited for the creation of an interactive imaging system. However, problems exist in implementation: How can evolutionary computation best support the holistic processes of human cognition? To answer this question requires an understanding of current theory regarding human holistic processes. Psychological Issues A well-known area in cognitive research that studies holistic processes is the recognition of objects and, in particular, the recognition of faces. Different perceptual encoding and representational processes have traditionally differentiated theories regarding the recognition of objects as compared to faces. However, the functional separation of these processes under all conditions of object recognition remains unclear (Bruce & Humphreys, 1994). Much of basic object recognition theory has been based on the decomposition of parts and the analysis of edge features (Marr & Nishihara, 1978; Biederman, 1987; Ullman, 1989). On the other hand, face recognition theory has been based on more holistic processes which utilize surface characteristics such as texture, color, and shading (Price & Humphreys, 1989). Some research suggests that the distinctions between object and face recognition begin to fade when one examines the object recognition processes of experts, who may utilize holistic processes similar to those found in face recognition (Diamond & Carey, 1986; Rhodes & McLean, 1990). The theory regarding holistic processing of faces can be separated into stronger and weaker stances (Bruce & Humphreys, 1994). Under the weaker stance, features may interact with each other through configural processes to form emergent properties or second-order relational features (Diamond & Carey, 1986). Under the stronger stance, face recognition is completely holistic; that is, its representation is non-decomposable in that no explicit description of features exists outside the context of the face (Tanaka & Farah, 1993). These stances provide two ways of approaching the development of an interactive system to support the holistic development of images: (1) A system which manipulates context-free features towards configuration, or (2) a system which develops the configuration of the image first, followed by more detailed development of features within the established context. We sought to design a recognition-driven system of the latter type, which would support the purely holistic development of images, including faces and objects. This system would function in a feature-free space to provide a