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) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface). In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems. ...

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. The user interface is the aggregate of means by which people (the users) interact with a particular machine, device, computer program or other complex tool (the system). ...

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. Karl Sims is a researcher formerly with the MIT Media Lab who is most well known for using genetic programming to evolve virtual creatures that competed in various simulated environments as described in this paper. ... In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute their innovative solutions to the evolutionary process. ...

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). In computer science, Evolution strategy (ES, from German Evolutionsstrategie) is an optimization technique based on ideas of adaptation and evolution. ... Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user-defined task. ... In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute their innovative solutions to the evolutionary process. ...

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). A genetic algorithm (or short GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems. ... A genetic algorithm (GA) is an algorithm used to find approximate solutions to difficult-to-solve problems through application of the principles of evolutionary biology to computer science. ... Genetic programming (GP) is an automated methodology inspired by biological evolution to find computer programs that best perform a user-defined task. ...

See also

• Evolutionary art
• Human-based evolutionary computation
• Human-based genetic algorithm
• Human-computer interaction
• Interactive evolutionary computation
• Karl Sims

An image generated using an evolutionary algorithm Evolutionary Art exploits the process of evolution to create an artwork which continually changes according to an evolutionary algorithm. ... Human-based evolutionary computation (HBEC) is a set of evolutionary computation techniques that rely on human innovation. ... In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute their innovative solutions to the evolutionary process. ... Human–computer interaction (HCI) or, alternatively, computer–human interaction (symbolized as Χ χ Chi, the 22nd letter of the Greek alphabet) is the study of interaction between people (users) and computers. ... Karl Sims is a researcher formerly with the MIT Media Lab who is most well known for using genetic programming to evolve virtual creatures that competed in various simulated environments as described in this paper. ...

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. (2000). Active user intervention in an EC Search. Proceesings of the JCIS 2000 [1]
• 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 [2]
• 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.

External links

• Galapagos by Karl Sims
• SBART, a program to evolve 2D images
• GenJam (Genetic Jammer)
• Evolutionary music
• darwin poetry
• Takagi Lab at Kyushu University
• [3] - Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms.
• idiofact.de, Webpage that uses interactive evolutionary computation with a generative design algorithm to generate 2d images.