Tim Holmes
ABSTRACT
Interactive Evolutionary Computation (IEC) has been applied to art and design problems where the fitness of an individual is at least partially subjective. Applications usually present a population from which the preferred individuals are selected before the usual evolutionary operations are performed to produce the next generation. Large population sizes and numbers of generations impose significant demands on the user. This paper proposes that selecting by means of eye movements could reduce user fatigue without sacrificing quality of fitness assessment. In the first experiment, an eye-tracker is used to capture fixations and confirm the reliability of such a measure of attention as a fitness driver for subjective evaluation such as aesthetic preference. In a second experiment, the robustness and efficiency of this technique is investigated for varying population sizes, presentation durations and levels of fitness sampling. The results and their consequences for future IEC applications are discussed.
- Bentley, P. J. 1996. Generic Evolutionary Design of Solid Objects using a Genetic Algorithm. Huddersfield, University of Huddersfield. PhD.Google Scholar
- Funes, P., and Pollack, J. 1999. Computer Evolution of Buildable Objects. Evolutionary Design by Computers. ed. P.J. Bentley. pp.387--404. San Francisco: Morgan Kaufmann.Google Scholar
- Todd, S., and Latham, W. Evolutionary Art and Computers. Academic Press, Inc. 1994. Google ScholarDigital Library
- Sims, K. 1991. Artificial Evolution for Computer Graphics. Computer Graphics, 25, 319--328. Google ScholarDigital Library
- Machwe A. T., and Parmee I. C. 2006. Integrating Aesthetic Criteria with Evolutionary Processes in Complex, Free-form Design - an Initial Investigation. IEEE Congress on Computational Intelligence, Vancouver, Canada.Google Scholar
- Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89 (9), 1275--1296.Google ScholarCross Ref
- Quiroz, J. C., Louis, S.J., and Dascalu, S.M. 2007. Interactive Evolution of XUL User Interfaces. Proceedings of Genetic and Evolutionary Computation Conference 2007, University College London. Google ScholarDigital Library
- Machwe,A. T, Parmee I. C,. 2006. Introducing Machine-learning within an Interactive Evolutionary Design. Environment. Design 2006. Dubrovnik, Croatia.Google Scholar
- Findlay, J.M and Gilchrist, I.D. 2003. Active vision: The psychology of looking and seeing. New York: Oxford University PressGoogle Scholar
- Snodgrass, J.G. 1975. Psychophysics. In: Experimental Sensory Psychology. B Scharf. (Ed.) pp. 17--67.Google Scholar
- Holmes, T., and Zanker, J. 2007. Evolving the Golden Ratio: A new method for an old question of aesthetic perception, Perception. Pion 36: 107Google Scholar
- Wolfe, J.M. 1994. Guided search 2.0 - a revised model of visual search. Psychonomic Bulletin and Review, 1, 202--238.Google ScholarCross Ref
- Rizzolatti, G., Riggio, L., Dascola, I., and Umiltà, C. 1987. Reorienting attention across the horizontal and vertical medians - evidence in favor of a premotor theory of attention. Neuropsychologia, 25, 31--40.Google ScholarCross Ref
- Klein, R.M. and Farrell, M. 1989. Search performance without eye-movements. Perception and Psychophysics, 46, 476--482.Google ScholarCross Ref
- Livio, M. 2002. The golden ratio: the story of phi, the extraordinary number of nature, art and beauty. London: Headline Review.Google Scholar
- Green, C.D. 1995. All That Gltters: A Review of Psychological Research on the Aesthetics of the Golden Section, Perception. Pion, 24, 937--968.Google ScholarCross Ref
- Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Boston, Addison Wesley Longman Inc. Google ScholarDigital Library
- Beyer, H.-G, and Schwefel, H.-P. 2002. Natural Computing 1: 3--52 Google ScholarDigital Library
- Joseph, J.S., and Optican, L.M. 1996. Involuntary attentional shifts due to orientation differences. Perception and Psychophysics,58(5), 651--665Google ScholarCross Ref
- Goldberg D.E., and Deb K. 1991. A comparative analysis of selection schemes used in genetic algorithms. Proceedings of Foundations of Genetic Algorithms FOGA-I. ed. G. Rawlings. pp.69--93. Morgan Kaufmann.Google Scholar
- Miller, B.L., and Goldberg, D.E. 1995. Genetic Algorithms, Tournament Selection, and the Effects of Noise, IlliGAL Report No.95006.Google Scholar
- Pallez, D., Collard, P., Baccino, T., and Dumercy, L. 2007. Eye-Tracking Evolutionary Algorithm to Minimize User Fatigue in IEC Applied to Interactive One-Max Problem. Proceedings of Genetic and Evolutionary Computation Conference 2007, University College London. Google ScholarDigital Library
- Deb, K. 2001. Multi-objective Optimization Using Evolutionary Algorithms. Chichester: Wiley. Google ScholarDigital Library
Index Terms
- Eye on the prize: using overt visual attention to drive fitness for interactive evolutionary computation
Recommendations
Eye-tracking evolutionary algorithm to minimize user fatigue in IEC applied to interactive one-max problem
GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computationIn this paper, we describe a new algorithm that consists in combining an eye-tracker for minimizing the fatigue of a user during the evaluation process of Interactive Evolutionary Computation. The approach is then applied to the Interactive One-Max ...
Reference chromosome to overcome user fatigue in IEC
AbstractEvolutionary Computation encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of the genetic diversity, for which it is necessary to work with large populations. ...
Using holey fitness landscapes to counteract premature convergence in evolutionary algorithms
GECCO '08: Proceedings of the 10th annual conference companion on Genetic and evolutionary computationPremature convergence is a persisting problem in evolutionary optimisation, in particular - genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue,...
Comments