Hands-free Evolution of 3D-printable Objects via Eye Tracking

Interactive evolution has shown the potential to create amazing and complex forms in both 2-D and 3-D settings. However, the algorithm is slow and users quickly become fatigued. We propose that the use of eye tracking for interactive evolution systems will both reduce user fatigue and improve evolutionary success. We describe a systematic method for testing the hypothesis that eye tracking driven interactive evolution will be a more successful and easier-to-use design method than traditional interactive evolution methods driven by mouse clicks. We provide preliminary results that support the possibility of this proposal, and lay out future work to investigate these advantages in extensive clinical trials.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Piero Mussio,et al.  Toward a Practice of Autonomous Systems , 1994 .

[3]  J. Hoffman,et al.  The role of visual attention in saccadic eye movements , 1995, Perception & psychophysics.

[4]  L. Optican,et al.  Involuntary attentional shifts due to orientation differences , 1996, Perception & psychophysics.

[5]  Gerald L. Lohse,et al.  Consumer Eye Movement Patterns on Yellow Pages Advertising , 1997 .

[6]  R. Pieters,et al.  Visual attention during brand choice : The impact of time pressure and task motivation , 1999 .

[7]  Ian C. Parmee,et al.  Multiobjective Satisfaction within an Interactive Evolutionary Design Environment , 2000, Evolutionary Computation.

[8]  Andrew J. Stewart,et al.  Integrating text and pictorial information: eye movements when looking at print advertisements. , 2001, Journal of experimental psychology. Applied.

[9]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[10]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[11]  Ying Zhang,et al.  Reduced human fatigue interactive evolutionary computation for micromachine design , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  I. C. Parmee,et al.  INTRODUCING MACHINE LEARNING WITHIN AN INTERACTIVE EVOLUTIONARY DESIGN ENVIRONMENT , 2006 .

[13]  John H. Frazer,et al.  Capturing aesthetic intention during interactive evolution , 2006, Comput. Aided Des..

[14]  Linden J. Ball,et al.  Eye tracking in HCI and usability research. , 2006 .

[15]  Kenneth O. Stanley,et al.  Generating large-scale neural networks through discovering geometric regularities , 2007, GECCO '07.

[16]  Philippe Collard,et al.  Eye-tracking evolutionary algorithm to minimize user fatigue in IEC applied to interactive one-max problem , 2007, GECCO '07.

[17]  Hod Lipson,et al.  Fab@Home: the personal desktop fabricator kit , 2007 .

[18]  Kenneth O. Stanley,et al.  Compositional Pattern Producing Networks : A Novel Abstraction of Development , 2007 .

[19]  Tim Holmes,et al.  Eye on the prize: using overt visual attention to drive fitness for interactive evolutionary computation , 2008, GECCO '08.

[20]  Jimmy Secretan,et al.  Picbreeder: evolving pictures collaboratively online , 2008, CHI.

[21]  Kenneth O. Stanley,et al.  Exploiting Open-Endedness to Solve Problems Through the Search for Novelty , 2008, ALIFE.

[22]  T. G. Chowdhury,et al.  The time-harried shopper: Exploring the differences between maximizers and satisficers , 2009 .

[23]  Kenneth O. Stanley,et al.  Evolving a diversity of virtual creatures through novelty search and local competition , 2011, GECCO '11.

[24]  Hod Lipson,et al.  Evolving three-dimensional objects with a generative encoding inspired by developmental biology , 2011, ECAL.

[25]  Kenneth O. Stanley,et al.  On the Performance of Indirect Encoding Across the Continuum of Regularity , 2011, IEEE Transactions on Evolutionary Computation.

[26]  Josh C. Bongard,et al.  Accelerating human-computer collaborative search through learning comparative and predictive user models , 2012, GECCO '12.