Interactive evolutionary computation

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Interactive evolutionary computation (IEC) or Aesthetic Selection is a general term for methods of evolutionary computation that use human evaluation. Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness; as in Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).

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[edit] IEC design issues

The number of evaluations that IEC can receive from one human user is limited by user fatigue which was reported by many researchers as a major problem. In addition, human evaluations are slow and expensive as compared to fitness function computation. Hence, one-user IEC methods should be designed to converge using a small number of evaluations, which necessarily implies very small populations. Several methods were proposed by researchers to speed up convergence, like interactive constrain evolutionary search (user intervention) or fitting user preferences using a convex function (Takagi, 2001). IEC human-computer interfaces should be carefully designed in order to reduce user fatigue.

However IEC implementations that can concurrently accept evaluations from many users overcome the limitations described above. An example of this approach is an interactive media installation by Karl Sims that allows to accept preference from many visitors by using floor sensors to evolve attractive 3D animated forms. Some of these multi-user IEC implementations serve as collaboration tools, for example HBGA.

[edit] IEC types

IEC methods include Interactive Evolution Strategy (Herdy, 1997), Interactive genetic algorithm (Caldwell, 1991), Interactive Genetic Programming (Sims, 1991; Tatsuo, 2000), and Human-based genetic algorithm (Kosorukoff, 2001).

[edit] IGA

An interactive genetic algorithm (IGA) is defined as a genetic algorithm that uses human evaluation. These algorithms belong to a more general category of Interactive evolutionary computation. The main application of these techniques include domains where it is hard or impossible to design a computational fitness function, for example, evolving images, music, various artistic designs and forms to fit a user's aesthetic preferences. Interactive computation methods can use different representations, both linear (as in traditional genetic algorithms) and tree-like ones (as in genetic programming).

[edit] See also

[edit] References

  • Dawkins, R. (1986), The Blind Watchmaker, Longman, 1986; Penguin Books 1988.
  • Caldwell, Craig and Victor S. Johnston (1991), Tracking a Criminal Suspect through "Face-Space" with a Genetic Algorithm, in Proceedings of the Fourth International Conference on Genetic Algorithm, Morgan Kaufmann Publisher, pp.416-421, July 1991.
  • Sims, K. (1991), Artificial Evolution for Computer Graphics. Computer Graphics 25(4), Siggraph '91 Proceedings, July 1991, pp.319-328.
  • Sims, K. (1991), Interactive Evolution of Dynamical Systems. First European Conference on Artificial Life, MIT Press
  • Herdy, M. (1997), Evolutionary Optimisation based on Subjective Selection – evolving blends of coffee. Proceedings 5th European Congress on Intelligent Techniques and Soft Computing (EUFIT’97); pp 2010-644.
  • Tatsuo, Unemi (2000). SBART 2.4: an IEC tool for creating 2D images, Movies and Collage, Proceedings of 2000 Genetic and Evolutionary Computational Conference workshop program, Las Vegas, Nevada, July 8, 2000, p.153
  • Kosorukoff, A. (2001), Human-based Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC-2001, 3464-3469.
  • Takagi, H. (2001). Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation. Proceesings of the IEEE 89, 9, pp. 1275-1296 [1]

[edit] External links