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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
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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
52:
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
291:*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
42:
is not known (for example, visual appeal or attractiveness; as in
Dawkins, 1986) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).
67:
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
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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,
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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.
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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
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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.
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artistic designs and forms to fit a user's aesthetic preferences. Interactive computation methods can use different representations, both linear (as in traditional
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614:"Picbreeder service, Collaborative interactive evolution allowing branching from other users' creations that produces pictures like faces and spaceships"
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2001 IEEE International
Conference on Systems, Man and Cybernetics. E-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236)
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Banzhaf, W. (1997), Interactive
Evolution, Entry C2.9, in: Handbook of Evolutionary Computation, Oxford University Press,
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Sims, K. (1991), Interactive
Evolution of Dynamical Systems. First European Conference on Artificial Life, MIT Press
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767:
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480:"EndlessForms.com, Collaborative interactive evolution allowing you to evolve 3D objects and have them 3D printed"
606:"Webpage that uses interactive evolutionary computation with a generative design algorithm to generate 2d images"
586:"Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms"
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Kruse, J.; Connor, A.M. (2015). "Multi-agent evolutionary systems for the generation of complex virtual worlds".
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196:"Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation"
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633:"Peer to Peer IGA Using collaborative IGA sessions for floorplanning and document design"
426:"GenYacht: An interactive generative design system for computer-aided yacht hull design"
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387:. Vol. 5. IEEE Transactions on Systems, Man, and Cybernetics. pp. 3464–3469.
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that use human evaluation. Usually human evaluation is necessary when the form of
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Sims, K. (1991). "Artificial
Evolution for Computer Graphics".
518:"Facial composite system using interactive genetic algorithms"
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310:
International
Journal of Information Theories and Applications
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617:
728:
Covariance Matrix
Adaptation Evolution Strategy (CMA-ES)
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Kosorukoff, A. (2001). "Human based genetic algorithm".
499:"Art by Evolution on the Web Interactive Art Generator"
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An interactive genetic algorithm (IGA) is defined as a
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khan, Shahroz; Gunpinar, Erkan; Sener, Bakir (2019).
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240:EAI Endorsed Transactions on Creative Technologies
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84:, interactive genetic algorithm, interactive
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918:No free lunch in search and optimization
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542:"SBART, a program to evolve 2D images"
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972:Interactive evolutionary computation
913:Interactive evolutionary computation
705:Interactive evolutionary computation
700:Human-based evolutionary computation
695:Evolutionary multimodal optimization
128:Human-based evolutionary computation
28:Interactive evolutionary computation
951:Evolutionary Computation (journal)
25:
578:"Takagi Lab at Kyushu University"
153:SCM-Synthetic Curriculum Modeling
34:is a general term for methods of
80:IEC methods include interactive
723:Cellular evolutionary algorithm
442:10.1016/j.oceaneng.2019.106462
1:
819:Bacterial Colony Optimization
262:10.4108/eai.20-10-2015.150099
133:Human-based genetic algorithm
90:human-based genetic algorithm
18:Interactive genetic algorithm
109:) and tree-like ones (as in
814:Particle swarm optimization
758:Gene expression programming
303:"Online Genetic Algorithms"
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778:Learning classifier system
768:Natural evolution strategy
138:Human–computer interaction
550:"GenJam (Genetic Jammer)"
393:10.1109/ICSMC.2001.972056
58:human–computer interfaces
977:Evolutionary computation
743:Evolutionary programming
690:Evolutionary data mining
671:Evolutionary computation
526:"Galapagos by Karl Sims"
36:evolutionary computation
873:Artificial intelligence
799:Ant colony optimization
203:Proceedings of the IEEE
868:Artificial development
738:Differential evolution
685:Evolutionary algorithm
903:Fitness approximation
888:Evolutionary robotics
829:Metaheuristic methods
351:10.1145/127719.122752
558:"Evolutionary music"
180:The Blind Watchmaker
177:Dawkins, R. (1986).
847:Gaussian adaptation
753:Genetic programming
301:Milani, A. (2004).
194:Takagi, H. (2001).
111:genetic programming
86:genetic programming
32:aesthetic selection
794:Swarm intelligence
787:Related techniques
763:Evolution strategy
733:Cultural algorithm
107:genetic algorithms
82:evolution strategy
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933:Program synthesis
908:Genetic operators
898:Fitness landscape
852:Memetic algorithm
837:Firefly algorithm
748:Genetic algorithm
430:Ocean Engineering
402:978-0-7803-7087-6
329:Computer Graphics
102:genetic algorithm
47:IEC design issues
16:(Redirected from
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923:Machine learning
893:Fitness function
883:Digital organism
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616:. Archived from
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928:Mating pool
678:Main Topics
158:User review
966:Categories
715:Algorithms
624:2007-08-02
596:2006-12-03
534:"E-volver"
509:2010-04-09
490:2011-06-18
436:: 106462.
253:1604.05792
183:. Longman.
164:References
450:204150911
337:CiteSeerX
143:Karl Sims
76:IEC types
65:Karl Sims
30:(IEC) or
943:Journals
411:13839604
316:: 20–28.
270:12670076
117:See also
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88:, and
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407:S2CID
373:p.153
306:(PDF)
266:S2CID
248:arXiv
199:(PDF)
463:ISBN
397:ISBN
70:HBGA
438:doi
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219:hdl
211:doi
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.