Interactive evolutionary computation: Difference between revisions

Revision as of 13:15, 22 April 2021

Revision as of 20:34, 22 April 2021

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==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}}</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]].

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IEC methods include interactive [[evolution strategy]],<ref>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.</ref> interactive genetic algorithm,<ref>*Caldwell, C. and Johnston, V.S. (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</ref><ref>{{cite journal|author=Milani, A.|year=2004|title=Online Genetic Algorithms|journal=International Journal of Information Theories and Applications|pages=20–28|url=http://sci-gems.math.bas.bg/jspui/bitstream/10525/838/1/ijita11-1-p04.pdf}}

</ref> interactive [[genetic programming]],<ref>*Sims, K. (1991), [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.226.7450&rep=rep1&type=pdf Artificial Evolution for Computer Graphics]. Computer Graphics 25(4), Siggraph '91 Proceedings, July 1991, pp.319-328.</ref><ref>Sims, K. (1991), Interactive Evolution of Dynamical Systems. First European Conference on Artificial Life, MIT Press

</ref><ref>Unemi, T. (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</ref> and [[human-based genetic algorithm]].,<ref>{{cite book|author=Kosorukoff, A.|year=2001|title=Human-based Genetic Algorithm|chapter=Human based genetic algorithm|journal=IEEE Transactions on Systems, Man, and Cybernetics |volume=5|pages=3464–3469|doi=10.1109/ICSMC.2001.972056|isbn=978-0-7803-7087-6|s2cid=13839604}}</ref>

===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.<ref>{{cite journal | last1= khan | first1= Shahroz | last2 = Gunpinar | first2 = Erkan |last3 = Sener | first3=Bakir | title= GenYacht: An interactive generative design system for computer-aided yacht hull design | journal= Ocean Engineering | volume= 191|pages= 106462 |year=2019|doi= h1010.1016/j.oceaneng.2019.106462| url= https://strathprints.strath.ac.uk/70007/ }}</ref> Interactive computation methods can use different representations, both linear (as in traditional [[genetic algorithms]]) and tree-like ones (as in [[genetic programming]]).

== See also ==