Editing Interactive evolutionary computation (section)

==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]].<ref>{{cite journal|author=Takagi, H.|year=2001|title=Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation|journal=Proceedings of the IEEE |volume=89|issue=9|pages=1275–1296|url=http://www.design.kyushu-u.ac.jp/~takagi/TAKAGI/IECpaper/ProcIEEE_3.pdf|doi=10.1109/5.949485|hdl=2324/1670053|hdl-access=free}}</ref> IEC [[human–computer interface]]s 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.<ref>{{cite journal|author1=Kruse, J.  |author2=Connor, A.M.|year=2015|title=Multi-agent evolutionary systems for the generation of complex virtual worlds|journal=EAI Endorsed Transactions on Creative Technologies |volume=15|issue=5|pages=150099|doi=10.4108/eai.20-10-2015.150099|arxiv=1604.05792|s2cid=12670076}}</ref>

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]].