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^[1]) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).

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.^[2] IEC human–computer interfaces should be carefully designed in order to reduce user fatigue. There is also evidence that the addition of computational agents can successfully counteract user fatigue.^[3]

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 one to accept preferences 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 methods include interactive evolution strategy,^[4] interactive genetic algorithm,^[5][6] interactive genetic programming,^[7][8][9] and human-based genetic algorithm.,^[10]

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.^[11] Interactive computation methods can use different representations, both linear (as in traditional genetic algorithms) and tree-like ones (as in genetic programming).

• Evolutionary art
• Human-based evolutionary computation
• Human-based genetic algorithm
• Human–computer interaction
• Karl Sims
• Electric Sheep
• SCM-Synthetic Curriculum Modeling
• User review

• Banzhaf, W. (1997), Interactive Evolution, Entry C2.9, in: Handbook of Evolutionary Computation, Oxford University Press, ISBN 978-0750308953

• "EndlessForms.com, Collaborative interactive evolution allowing you to evolve 3D objects and have them 3D printed". Archived from the original on 2018-11-14. Retrieved 2011-06-18.
• "Art by Evolution on the Web Interactive Art Generator". Archived from the original on 2018-04-15. Retrieved 2010-04-09.
• "Facial composite system using interactive genetic algorithms".
• "Galapagos by Karl Sims".
• "E-volver".
• "SBART, a program to evolve 2D images".
• "GenJam (Genetic Jammer)".
• "Evolutionary music".
• "Darwin poetry". Archived from the original on 2006-04-12.
• "Takagi Lab at Kyushu University".
• "Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms". Archived from the original on 2011-07-09. Retrieved 2006-12-03..
• "Webpage that uses interactive evolutionary computation with a generative design algorithm to generate 2d images".
• "Picbreeder service, Collaborative interactive evolution allowing branching from other users' creations that produces pictures like faces and spaceships". Archived from the original on 2011-07-25. Retrieved 2007-08-02.
• "Peer to Peer IGA Using collaborative IGA sessions for floorplanning and document design".