Interactive evolutionary computation (IEC) 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) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).
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 constraing evolutionary search (user intervention) or fitting user preferences using a convex function (Takagi, 2001).
However IEC implementations that can concurrenlty 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.
IEC implementations
Examples of IEC methods include Interactive genetic algorithm and Human-based genetic algorithm.
See also
References
- Dawkins R. (1986), The Blind Watchmaker, Longman, 1986; Penguin Books 1988.
- 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
- Craig Caldwell 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.
- J. A. Biles (1994). "GenJam: A Genetic Algorithm for Generating Jazz Solos," In Proceedings of the 1994 International Computer Music Conference, ICMA, San Francisco, 1994.
- 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 640-644.
- 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: Fushion of the Capacities of EC Optimization and Human Evaluation. Proceesings of the IEEE 89, 9, pp. 1275-1296
- Kosorukoff, A., Goldberg, D. E. (2002). Evolutionary Computation As A Form Of Organization. GECCO 2002: 965-972
- Parmee I. C. (2002) Supporting Innovation and Creativity through Interactive Evolutionary Systems. Poster Proceedings Creativity and Cognition 4 Conference, University of Loughborough, CHI Conference Publications.
- Parmee I. C., (2002), Improving Problem Definition through Interactive Evolutionary Computation, Journal of Artificial Intelligence in Engineering Design, Analysis and Manufacture - Special Issue: Human-computer Interaction in Engineering, 16(3)
- Cheng, C. D., Kosorukoff, A. (2004), Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. Genetic and Evolutionary Computational Conference, GECCO-2004.