Evolución cultural en sociedades artificiales

Evolución cultural en sociedades artificiales

 En este artículo se revisan las generalidades de algunos modelos computacionales inspirados en características propias de la evolución cultural y se clasifican en dos grupos, según se fundamenten o no en la memética. Se pretende demostrar, a pesar del auge que han tenido en aplicaciones orien­tadas a solucionar problemas de optimización, su falta de naturalidad frente a la posibilidad de simular características culturales presentes en las sociedades naturales. La cultura se asume como el conjunto de ideas y comportamientos desarrollados y transmitidos mediante la interacción entre agentes portadores de genes y memes, pues se considera que estos tienen genéticamente codificada una maquinaria biológica con cierto sistema mental... Ver más

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2346-139X

Sterpin Buitrago, Dante Giovanni

CORPORACIÓN UNIFICADA NACIONAL DE EDUCACIÓN SUPERIOR

10.52143/2346139X.610

2018-08-04

31

45

Colombia

Sociedades Artificiales

Modelos Computacionales

Memética

Memes

Genes

Evolución Cultural

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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

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Krasnogor, N. y Gustafson, S. (2002). Toward truly “memetic” memetic algorithms: Discussion and proofs of concept. En D. Corne, G. Fogel, W. Hart, J. Knowles, N. Krasnogor, R. Roy, J. Smith y A. Tiwari (eds.), Advances in Nature-Inspired Computation: The ppsn vii Workshops, (pp. 9-10). Reading: pedal; University of Reading.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Pasadena, Estados Unidos: Caltech. Recuperado de https://bit.ly/38K6hsb
Meuth, R., Lim, M., Ong, Y. y Wunsch, D. (2009). A proposition on memes and meta-memes incomputing for higher-order learning. Memetic Computing, 1(2), 85-100.
Maldonado, C. y Gómez, N. (2010). Modelamiento y simulación de sistemas complejos. Bogotá: Editorial Universidad del Rosario. Recuperado de https://bit.ly/36JK4sD
Lewontin, R. (1970). The Units of Selection. Annual Review of Ecology and Systematics, 1, 1-18.
Le, M., Ong, Y., Jin, Y. y Sendhoff, B. (2009). Lamarckian memetic algorithms: Local optimum and connectivity structure analysis. Memetic Computing, 1(3), 175-190. doi: https://doi.org/10.1007/s12293-009-0016-9
Le, M., Neri, F. y Ong, Y. (2015). Memetic algorithms. En H. Ishibuchi (ed.), Encyclopedia of Life Support Systems: Computational Intelligence (vol. 2), (pp. 57-86). Singapur: Unesco; Eolss Publishers.
Lamma, E., Riguzzi, F. y Pereira, L. (2003). Belief revision via lamarckian evolution. New Generation Computing, 21(3), 247-275. DOI: https://doi.org/10.1007/BF03037475
Krasnogor, N. y Smith, J. (2005). A tutorial for competent memetic algorithms: models, taxonomy and design issues. ieee Trans Evol Comput, 9, 474-488.
Krasnogor, N. y Gustafson, S. (2004). A study on the use of “self-generation” in memetic algorithms. Natural Computing, 3(1), 53-76. doi: https://doi.org/10.1023/B:NACO.0000023419.83147.67
Krasnogor, N., Aragón, A. y Pacheco, J. (2006). Memetic algorithms. En E. Alba y R. Martí (eds.), Metaheuristic Procedures for Training Neutral Networks (pp. 225-248). Boston: Springer. doi:https://doi.org/10.1007/0-387-33416-5
Nguyen, Q., Ong, Y. y Lim, M. (Julio del 2008). Non-genetic transmission of memes by diffusion. Conferencia presentada en 10th annual conference on Genetic and evolutionary computation, Atlanta, Estados Unidos, 1017-1024. doi: http://doi.acm.org/10.1145/1389095.1389285
Kendall, G., Cowling, P. y Soubeiga, E. (2002). Choice function and random hyperheuristics. Conferencia presentada en 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, 667-671. Recuperado de https://bit.ly/2PSE9dU
Huy, N., Soon, O., Hiot, L. y Krasnogor, N. (2009). Adaptive cellular memetic algorithms. Evolutionary Computation, 17(2), 231-256. doi: https://doi.org/10.1162/evco.2009.17.2.231
Holland, J. (1975). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Míchigan: University of Michigan Press.
Heinerman, J., Rango, M. y Eiben, A. (Diciembre del 2015). Evolution, individual learning, and social learning in a swarm of real robots. Conferencia presentada en 2015 ieee Symposium Series on Computational Intelligence, Cape Town, Sur Africa, 1055-1062. doi: https://doi.org/10.1109/SSCI.2015.152
Hauser, M. (2006). Moral Minds: How Nature Designed our Universal Sense of Right and Wrong.Nueva York: Harper Collins.
Hart, W., Krasnogor, N. y Smith, J. (2005). Memetic evolutionary algorithms. En W. Hart, N. Krasnogor y J. Smith. (eds.), Recent Advances in Memetic Algorithms (pp. 3-27). Berlin; Heidelberg; New York: Springer.
González, S. (2004). ¿Sociedades artificiales? Una introducción a la simulación social. Revista Internacional de Sociología, 62(39), 199-222. Recuperado de https://bit.ly/2PpCa1y
Goldberg, D. (1989). Genetic algorithms in search, optimization, and machine learning. Boston: Addison-Wesley Longman Publishing Co. Gong, T. (2010). Exploring the roles of horizontal, vertical, and oblique transmissions in language evolution. Adaptive Behavior, 18(3-4), 356-376.
Floreano, D., Husbands, P. y Nolfi, S. (2008). Evolutionary robotics. En B. Siciliano y O. Khatib(eds.), Springer handbook of robotics (pp. 1423-1451). Berlin: Springer.
Ferber, J. (1999). Multi-Agent System: An Introduction to Distributed Artificial Intelligence. Boston: Addison-Wesley Longman Publishing Co.
Nedjah, N., Coelho, L. y Mourelle, L. (Eds.). (2007). Mobile robots: The evolutionary approach - Studies in Computational Intelligence (vol. 50). Berlin: Springer.
Pam, N. (13 de abril del 2013). Sociality [recurso en línea]. Recuperado de https://bit.ly/34vpt9Q
Espingardeiro, A. (2014). A roboethics framework for the development and introduction of social assistive robots in elderly care (tesis de doctorado). Universidad de Salford, Manchester. Recuperado de https://bit.ly/2toFxNQ
Veruggio, G., Solis, J. y Van der Loos, M. (2011). Roboethics: Ethics Applied to Robotics. ieee Robotics & Automation Magazine, 18(1), 21-22. doi: 10.1109/MRA.2010.940149
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Zaikman, Y. y Marks, M. (2014). Ambivalent Sexism and the Sexual Double Standard. Sex Roles, 71(9-10), 333-344.
Wooldridge, M. (2009). An introduction to multiagent systems. Glasgow: John Wiley & Sons.
Ullah, A., Sarker, R., Comfort, D. y Lokan, C. (Septiembre del 2007). An agent-based memetic algorithm (ama) for solving constrained optimization problems. Conferencia presentada en 2007 ieee Congress on Evolutionary Computation, Singapur, Singapur, 999-1006. doi: https://doi.org/10.1109/CEC.2007.4424579
Pan, Z., Feng, L., Ong, Y., Kang, Y., Tan, A. y Miao, C. (Septiembre del 2010). Meme selection, variation, and transmission in multi-agent system. Conferencia presentada en World Automation Congress, Kobe, Japón, 1-6. Recuperado de https://bit.ly/2rXpo1j
Tan, A., Lu, N. y Xiao, D. (2008). Integrating temporal difference methods and self-organizing neural networks for reinforcement learning with delayed evaluative feedback. ieee Transactions on Neural Networks, 19(2), 230-244. doi: https://doi.org/10.1109/TNN.2007.905839
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12
Español
https://revistas.cun.edu.co/index.php/hashtag/article/view/610
Fondo Editorial CUN
application/pdf
Artículo de revista
Núm. 12 , Año 2018 : Revista Hashtag 2018A
Sociedades Artificiales
https://creativecommons.org/licenses/by-nc-nd/4.0/ - 2018
Modelos Computacionales
Memética
Memes
Genes
Evolución Cultural
Sterpin Buitrago, Dante Giovanni
 En este artículo se revisan las generalidades de algunos modelos computacionales inspirados en características propias de la evolución cultural y se clasifican en dos grupos, según se fundamenten o no en la memética. Se pretende demostrar, a pesar del auge que han tenido en aplicaciones orien­tadas a solucionar problemas de optimización, su falta de naturalidad frente a la posibilidad de simular características culturales presentes en las sociedades naturales. La cultura se asume como el conjunto de ideas y comportamientos desarrollados y transmitidos mediante la interacción entre agentes portadores de genes y memes, pues se considera que estos tienen genéticamente codificada una maquinaria biológica con cierto sistema mental y que en este sistema están los memes capaces de comunicarse con otros agentes semejantes. Independientemente del hecho de tener neuronas en este sistema, en la mayoría de los modelos acá revisados no hay una relación genético-cultural de este tipo, pues en ellos lo cultural queda reducido a acelerar la resolución genética de problemas; o, en el mejor caso encontrado, lo cultural emplea memes derivados de los genes, pero como copias de su información génica
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Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Bongard, J. (2013). Evolutionary robotics. Communications of the acm, 56(8), 74-83.
Dawkins, R. (1993). Viruses of the mind. En B. Dahlbom (ed.), Dennett and His Critics: Demystifying Mind (pp. 13-27). Oxford: Blackwell.
Dawkins, R. (1986). The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design. Nueva York y Londres: WW Norton & Company.
Dawkins, R. (1983). Universal Darwinism. En D. Bendall (ed.), Evolution from Molecules to Man (pp. 403-425). Nueva York: Cambridge University Press.
Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.
Curran, D. y O’Riordan, C. (2007). The effects of cultural learning in populations of neural networks. Artificial Life, 13(1), 45-67. doi: https://doi.org/10.1162/artl.2007.13.1.45
Cowling, P., Kendall, G. y Soubeiga, E. (2001). A Hyperheuristic Approach to Scheduling a Sa¬les Summit. En E. Burke y W. Erben (eds.), Practice and Theory of Automated Timetabling iii. patat 2000. Lecture Notes in Computer Science (vol. 2079) (pp. 176-190), Konstanz, Germany: Springer.
Chen, X., Ong, Y., Lim, M. y Tan, K. (2011). A multi-facet survey on memetic computation. IEEE Transactions on evolutionary computation, 15(5), 591-607. doi: https://doi.org/10.1109/ TEVC.2011.2132725
Burke, E., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Özcan, E. y Qu, R. (2013). Hyper-heu¬ristics: a survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695- 1724. doi: https://doi.org/10.1057/jors.2013.71
Brodie, R. (2009). Virus of the Mind: The New Science of the Meme. Nueva York: Hay House.
Borenstein, E. y Ruppin E. (2004). Envolving imitating agents and the emergence of a neural mi¬rror system. En M. Bedau, P. Husbands, T. Ikegami, J. Pollack y R. Watson (eds.), Artificial life ix. Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (pp. 146-151). Cambridge, Estados Unidos: The mit press.
Publication
Blackmore, S. (1999). The Meme Machine. Oxford: Oxford University Press.
Acerbi, A. y Marocco, D. (Junio del 2009). Orienting learning by exploiting sociality: An evolutio¬nary robotics simulation. Conferencia presentada en 2009 International Joint Conference on Neural Networks, Atlanta, Estados Unidos, 20-27. doi: https://doi.org/10.1109/ IJCNN.2009.5178607
Aunger, R. (2002). The Electric Meme: A New Theory of How We Think. Nueva York: Free Press.
Computational Models
In this paper, the generalities of some computational models inspired by characteristics of cultural evolution are reviewed and classified into two groups, depending on whether they are based on me­metics or not. It is intended to demonstrate, despite the boom they have had in applications aimed to solve optimization problems, their lack of naturalness against the possibility of simulating cultural characteristics present in natural societies. The culture is assumed as the set of ideas and behaviors developed and transmitted through the interaction between agents carrying genes and memes, since it is considered that they have genetically encoded a biological machinery with a certain mental system and that in this system are the memes capable of communicate with other similar agents. Regardless of the fact of having neurons in this system, in most of the models reviewed here there is no such genetic-cultural relationship, since in them the cultural is reduced to accelerate the genetic resolution of problems or, in the best case found, the cultural uses memes derived from genes, but as copies of their gene information.
Evolución cultural en sociedades artificiales
Artificial Societies
Journal article
Cultural Evolution
Genes
Memes
Memetics
2018-08-04T00:00:00Z
https://revistas.cun.edu.co/index.php/hashtag/article/download/610/449
31
2346-139X
2018-08-04
10.52143/2346139X.610
https://doi.org/10.52143/2346139X.610
2018-08-04T00:00:00Z
45
institution CORPORACIÓN UNIFICADA NACIONAL DE EDUCACIÓN SUPERIOR
thumbnail https://nuevo.metarevistas.org/CORPORACIONUNIFICADANACIONALDEEDUCACIONSUPERIOR/logo.png
country_str Colombia
collection #ashtag
title Evolución cultural en sociedades artificiales
spellingShingle Evolución cultural en sociedades artificiales
Sterpin Buitrago, Dante Giovanni
Sociedades Artificiales
Modelos Computacionales
Memética
Memes
Genes
Evolución Cultural
Computational Models
Artificial Societies
Cultural Evolution
Genes
Memes
Memetics
title_short Evolución cultural en sociedades artificiales
title_full Evolución cultural en sociedades artificiales
title_fullStr Evolución cultural en sociedades artificiales
title_full_unstemmed Evolución cultural en sociedades artificiales
title_sort evolución cultural en sociedades artificiales
title_eng Evolución cultural en sociedades artificiales
description  En este artículo se revisan las generalidades de algunos modelos computacionales inspirados en características propias de la evolución cultural y se clasifican en dos grupos, según se fundamenten o no en la memética. Se pretende demostrar, a pesar del auge que han tenido en aplicaciones orien­tadas a solucionar problemas de optimización, su falta de naturalidad frente a la posibilidad de simular características culturales presentes en las sociedades naturales. La cultura se asume como el conjunto de ideas y comportamientos desarrollados y transmitidos mediante la interacción entre agentes portadores de genes y memes, pues se considera que estos tienen genéticamente codificada una maquinaria biológica con cierto sistema mental y que en este sistema están los memes capaces de comunicarse con otros agentes semejantes. Independientemente del hecho de tener neuronas en este sistema, en la mayoría de los modelos acá revisados no hay una relación genético-cultural de este tipo, pues en ellos lo cultural queda reducido a acelerar la resolución genética de problemas; o, en el mejor caso encontrado, lo cultural emplea memes derivados de los genes, pero como copias de su información génica
description_eng In this paper, the generalities of some computational models inspired by characteristics of cultural evolution are reviewed and classified into two groups, depending on whether they are based on me­metics or not. It is intended to demonstrate, despite the boom they have had in applications aimed to solve optimization problems, their lack of naturalness against the possibility of simulating cultural characteristics present in natural societies. The culture is assumed as the set of ideas and behaviors developed and transmitted through the interaction between agents carrying genes and memes, since it is considered that they have genetically encoded a biological machinery with a certain mental system and that in this system are the memes capable of communicate with other similar agents. Regardless of the fact of having neurons in this system, in most of the models reviewed here there is no such genetic-cultural relationship, since in them the cultural is reduced to accelerate the genetic resolution of problems or, in the best case found, the cultural uses memes derived from genes, but as copies of their gene information.
author Sterpin Buitrago, Dante Giovanni
author_facet Sterpin Buitrago, Dante Giovanni
topicspa_str_mv Sociedades Artificiales
Modelos Computacionales
Memética
Memes
Genes
Evolución Cultural
topic Sociedades Artificiales
Modelos Computacionales
Memética
Memes
Genes
Evolución Cultural
Computational Models
Artificial Societies
Cultural Evolution
Genes
Memes
Memetics
topic_facet Sociedades Artificiales
Modelos Computacionales
Memética
Memes
Genes
Evolución Cultural
Computational Models
Artificial Societies
Cultural Evolution
Genes
Memes
Memetics
citationissue 12
citationedition Núm. 12 , Año 2018 : Revista Hashtag 2018A
publisher Fondo Editorial CUN
ispartofjournal #ashtag
source https://revistas.cun.edu.co/index.php/hashtag/article/view/610
language Español
format Article
rights http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/ - 2018
https://creativecommons.org/licenses/by-nc-nd/4.0/
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
references Krasnogor, N. y Gustafson, S. (2002). Toward truly “memetic” memetic algorithms: Discussion and proofs of concept. En D. Corne, G. Fogel, W. Hart, J. Knowles, N. Krasnogor, R. Roy, J. Smith y A. Tiwari (eds.), Advances in Nature-Inspired Computation: The ppsn vii Workshops, (pp. 9-10). Reading: pedal; University of Reading.
Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Pasadena, Estados Unidos: Caltech. Recuperado de https://bit.ly/38K6hsb
Meuth, R., Lim, M., Ong, Y. y Wunsch, D. (2009). A proposition on memes and meta-memes incomputing for higher-order learning. Memetic Computing, 1(2), 85-100.
Maldonado, C. y Gómez, N. (2010). Modelamiento y simulación de sistemas complejos. Bogotá: Editorial Universidad del Rosario. Recuperado de https://bit.ly/36JK4sD
Lewontin, R. (1970). The Units of Selection. Annual Review of Ecology and Systematics, 1, 1-18.
Le, M., Ong, Y., Jin, Y. y Sendhoff, B. (2009). Lamarckian memetic algorithms: Local optimum and connectivity structure analysis. Memetic Computing, 1(3), 175-190. doi: https://doi.org/10.1007/s12293-009-0016-9
Le, M., Neri, F. y Ong, Y. (2015). Memetic algorithms. En H. Ishibuchi (ed.), Encyclopedia of Life Support Systems: Computational Intelligence (vol. 2), (pp. 57-86). Singapur: Unesco; Eolss Publishers.
Lamma, E., Riguzzi, F. y Pereira, L. (2003). Belief revision via lamarckian evolution. New Generation Computing, 21(3), 247-275. DOI: https://doi.org/10.1007/BF03037475
Krasnogor, N. y Smith, J. (2005). A tutorial for competent memetic algorithms: models, taxonomy and design issues. ieee Trans Evol Comput, 9, 474-488.
Krasnogor, N. y Gustafson, S. (2004). A study on the use of “self-generation” in memetic algorithms. Natural Computing, 3(1), 53-76. doi: https://doi.org/10.1023/B:NACO.0000023419.83147.67
Krasnogor, N., Aragón, A. y Pacheco, J. (2006). Memetic algorithms. En E. Alba y R. Martí (eds.), Metaheuristic Procedures for Training Neutral Networks (pp. 225-248). Boston: Springer. doi:https://doi.org/10.1007/0-387-33416-5
Nguyen, Q., Ong, Y. y Lim, M. (Julio del 2008). Non-genetic transmission of memes by diffusion. Conferencia presentada en 10th annual conference on Genetic and evolutionary computation, Atlanta, Estados Unidos, 1017-1024. doi: http://doi.acm.org/10.1145/1389095.1389285
Kendall, G., Cowling, P. y Soubeiga, E. (2002). Choice function and random hyperheuristics. Conferencia presentada en 4th Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, 667-671. Recuperado de https://bit.ly/2PSE9dU
Huy, N., Soon, O., Hiot, L. y Krasnogor, N. (2009). Adaptive cellular memetic algorithms. Evolutionary Computation, 17(2), 231-256. doi: https://doi.org/10.1162/evco.2009.17.2.231
Holland, J. (1975). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Míchigan: University of Michigan Press.
Heinerman, J., Rango, M. y Eiben, A. (Diciembre del 2015). Evolution, individual learning, and social learning in a swarm of real robots. Conferencia presentada en 2015 ieee Symposium Series on Computational Intelligence, Cape Town, Sur Africa, 1055-1062. doi: https://doi.org/10.1109/SSCI.2015.152
Hauser, M. (2006). Moral Minds: How Nature Designed our Universal Sense of Right and Wrong.Nueva York: Harper Collins.
Hart, W., Krasnogor, N. y Smith, J. (2005). Memetic evolutionary algorithms. En W. Hart, N. Krasnogor y J. Smith. (eds.), Recent Advances in Memetic Algorithms (pp. 3-27). Berlin; Heidelberg; New York: Springer.
González, S. (2004). ¿Sociedades artificiales? Una introducción a la simulación social. Revista Internacional de Sociología, 62(39), 199-222. Recuperado de https://bit.ly/2PpCa1y
Goldberg, D. (1989). Genetic algorithms in search, optimization, and machine learning. Boston: Addison-Wesley Longman Publishing Co. Gong, T. (2010). Exploring the roles of horizontal, vertical, and oblique transmissions in language evolution. Adaptive Behavior, 18(3-4), 356-376.
Floreano, D., Husbands, P. y Nolfi, S. (2008). Evolutionary robotics. En B. Siciliano y O. Khatib(eds.), Springer handbook of robotics (pp. 1423-1451). Berlin: Springer.
Ferber, J. (1999). Multi-Agent System: An Introduction to Distributed Artificial Intelligence. Boston: Addison-Wesley Longman Publishing Co.
Nedjah, N., Coelho, L. y Mourelle, L. (Eds.). (2007). Mobile robots: The evolutionary approach - Studies in Computational Intelligence (vol. 50). Berlin: Springer.
Pam, N. (13 de abril del 2013). Sociality [recurso en línea]. Recuperado de https://bit.ly/34vpt9Q
Espingardeiro, A. (2014). A roboethics framework for the development and introduction of social assistive robots in elderly care (tesis de doctorado). Universidad de Salford, Manchester. Recuperado de https://bit.ly/2toFxNQ
Veruggio, G., Solis, J. y Van der Loos, M. (2011). Roboethics: Ethics Applied to Robotics. ieee Robotics & Automation Magazine, 18(1), 21-22. doi: 10.1109/MRA.2010.940149
Zaikman, Y. y Marks, M. (2014). Ambivalent Sexism and the Sexual Double Standard. Sex Roles, 71(9-10), 333-344.
Wooldridge, M. (2009). An introduction to multiagent systems. Glasgow: John Wiley & Sons.
Ullah, A., Sarker, R., Comfort, D. y Lokan, C. (Septiembre del 2007). An agent-based memetic algorithm (ama) for solving constrained optimization problems. Conferencia presentada en 2007 ieee Congress on Evolutionary Computation, Singapur, Singapur, 999-1006. doi: https://doi.org/10.1109/CEC.2007.4424579
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