Human-based genetic algorithm - Revision history

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	*[[Human-based computation]]		*[[Human-based computation]]
	*[[Human-based evolutionary computation]]		*[[Human-based evolutionary computation]]
	*[[~~Human-computer~~ interaction]]		*[[Human–computer interaction]]
	*[[Interactive genetic algorithm]]		*[[Interactive genetic algorithm]]
	*[[Memetics]]		*[[Memetics]]

Rckjdavies: /* External links */

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External links

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	==External links==		==External links==
	* [http://www.3form.com Free Knowledge Exchange], a project using HBGA for collaborative solving of problems expressed in natural language.		* [http://www.3form.com Free Knowledge Exchange], a project using HBGA for collaborative solving of problems expressed in natural language.
			* [https://mscinnovations.wordpress.com/ ParEvo], ParEvo is a method of developing alternative future scenarios, using a participatory evolutionary process

	{{DEFAULTSORT:Human-Based Genetic Algorithm}}		{{DEFAULTSORT:Human-Based Genetic Algorithm}}

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	Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.		Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.

	Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal=EAI Endorsed Transactions on Creative Technologies 15\|volume=2\|issue=5\|~~pages~~=150099\|doi=10.4108/eai.20-10-2015.150099\|arxiv=1604.05792}}</ref>		Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|s2cid=12670076\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal= EAI Endorsed Transactions on Creative Technologies\|volume= 2\|issue= 5\|page=150099\|doi=10.4108/eai.20-10-2015.150099\|arxiv=1604.05792}}</ref>

	==See also==		==See also==

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	Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.		Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.

	Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal=EAI Endorsed Transactions on Creative Technologies 15\|volume=2\|issue=5\|pages=150099\|doi=10.4108/eai.20-10-2015.150099}}</ref>		Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal=EAI Endorsed Transactions on Creative Technologies 15\|volume=2\|issue=5\|pages=150099\|doi=10.4108/eai.20-10-2015.150099\|arxiv=1604.05792}}</ref>

	==See also==		==See also==

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← Previous revision		Revision as of 13:13, 18 February 2019
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	Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.		Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.

	Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal=EAI Endorsed Transactions on Creative Technologies 15\|issue=5\|~~url~~=~~http://eudl.eu/pdf/10.4108/eai.20-10-2015.~~150099\|doi=10.4108/eai.20-10-2015.150099}}</ref>		Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by [[interactive genetic algorithms]].<ref>{{cite journal\|author=Kruse, J.\|author2=Connor, A.\|year=2015\|title=Multi-agent evolutionary systems for the generation of complex virtual worlds\|journal=EAI Endorsed Transactions on Creative Technologies 15\|volume=2\|issue=5\|pages=150099\|doi=10.4108/eai.20-10-2015.150099}}</ref>

	==See also==		==See also==

Omnipaedista at 05:19, 10 September 2018

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	In [[evolutionary computation]], a '''human-based genetic algorithm''' (HBGA) is a [[genetic algorithm]] that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.		In [[evolutionary computation]], a '''human-based genetic algorithm''' ('''HBGA''') is a [[genetic algorithm]] that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans.

	==Evolutionary genetic systems and human agency==		==Evolutionary genetic systems and human agency==

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	*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%2Bkosorukoff/hbga/hbga.html online]		*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%2Bkosorukoff/hbga/hbga.html online]
	*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [https://dx.doi.org/10.1109/ICSMC.2001.972056 full text]		*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [https://dx.doi.org/10.1109/ICSMC.2001.972056 full text]
	*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [~~http~~://~~www~~.derrickcheng.com/Project/HBGA full text]		*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [https://web.archive.org/web/20110709014211/http://derrickcheng.com/Project/HBGA/ full text]
	*Milani, Alfredo (2004). [http://sci-gems.math.bas.bg/jspui/bitstream/10525/838/1/ijita11-1-p04.pdf Online Genetic Algorithms]. ''International Journal of Information Theories and Applications'' pp. 20–28		*Milani, Alfredo (2004). [http://sci-gems.math.bas.bg/jspui/bitstream/10525/838/1/ijita11-1-p04.pdf Online Genetic Algorithms]. ''International Journal of Information Theories and Applications'' pp. 20–28
	*Milani, Alfredo and Silvia Suriani (2004), ''[http://doi.ieeecomputersociety.org/10.1109/WI.2004.10021 ADAN: Adaptive Newspapers based on Evolutionary Programming]'' In IEEE/WIC/ACM International Conference on Web Intelligence,(WI'04), pp. 779–780, IEEE Press, 2004		*Milani, Alfredo and Silvia Suriani (2004), ''[http://doi.ieeecomputersociety.org/10.1109/WI.2004.10021 ADAN: Adaptive Newspapers based on Evolutionary Programming]'' In IEEE/WIC/ACM International Conference on Web Intelligence,(WI'04), pp. 779–780, IEEE Press, 2004

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	*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/http://www.3form.com/formula/whatis.htm internet archive]		*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/http://www.3form.com/formula/whatis.htm internet archive]
	*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%2Bkosorukoff/hbga/hbga.html online]		*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%2Bkosorukoff/hbga/hbga.html online]
	*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [~~http~~://dx.doi.org/10.1109/ICSMC.2001.972056 full text]		*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [https://dx.doi.org/10.1109/ICSMC.2001.972056 full text]
	*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]		*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]
	*Milani, Alfredo (2004). [http://sci-gems.math.bas.bg/jspui/bitstream/10525/838/1/ijita11-1-p04.pdf Online Genetic Algorithms]. ''International Journal of Information Theories and Applications'' pp. 20–28		*Milani, Alfredo (2004). [http://sci-gems.math.bas.bg/jspui/bitstream/10525/838/1/ijita11-1-p04.pdf Online Genetic Algorithms]. ''International Journal of Information Theories and Applications'' pp. 20–28

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	*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/http://www.3form.com/formula/whatis.htm internet archive]		*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/http://www.3form.com/formula/whatis.htm internet archive]
	*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%~~20kosorukoff~~/hbga/hbga.html online]		*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%2Bkosorukoff/hbga/hbga.html online]
	*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [http://dx.doi.org/10.1109/ICSMC.2001.972056 full text]		*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [http://dx.doi.org/10.1109/ICSMC.2001.972056 full text]
	*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]		*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]

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	{{reflist}}		{{reflist}}

	*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/www.3form.com/formula/whatis.htm internet archive]		*Kosorukoff, Alex (1999). Free knowledge exchange. [https://web.archive.org/web/19990824183328/http://www.3form.com/formula/whatis.htm internet archive]
	*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex~~+kosorukoff~~/hbga/hbga.html online]		*Kosorukoff, Alex (2000). Human-based genetic algorithm. [https://web.archive.org/web/20091027041228/http://geocities.com/alex%20kosorukoff/hbga/hbga.html online]
	*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [http://dx.doi.org/10.1109/ICSMC.2001.972056 full text]		*Kosorukoff, Alex (2001). Human-based genetic algorithm. In ''IEEE International Conference on Systems, Man, and Cybernetics'', SMC-2001, 3464-3469. [http://dx.doi.org/10.1109/ICSMC.2001.972056 full text]
	*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]		*Cheng, Chihyung Derrick and Alex Kosorukoff (2004). Interactive one-max problem allows to compare the performance of interactive and human-based genetic algorithms. In ''Genetic and Evolutionary Computational Conference'', GECCO-2004. [http://www.derrickcheng.com/Project/HBGA full text]