Model

• al-Rifaie, M. M., Bishop, J. M., & Caines, S. (2012). Creativity and autonomy in swarm intelligence systems. Cognitive Computation, 4(3), 320–331. https://doi.org/10.1007/s12559-012-9130-y.

  Article  Google Scholar 

• al-Rifaie, M. M., & Bishop, J. M. (2013a, April). Swarmic sketches and attention mechanism. In International Conference on Evolutionary and Biologically Inspired Music and Art (pp. 8596). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36955-1_8.

• al-Rifaie, M. M., & Bishop, J. M. (2013b). Swarmic paintings and colour attention. In International Conference on Evolutionary and Biologically Inspired Music and Art (pp. 97–108). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-36955-1_9.

• al-Rifaie, A. M., & Al-Rifaie, M. M. (2015). Generative music with stochastic diffusion search. In International Conference on Evolutionary and Biologically Inspired Music and Art (pp. 1–14). Cham: Springer. https://doi.org/10.1007/978-3-319-16498-4_1.

• Aupetit, S., Bordeau, V., Monmarché, N., Slimane, M., & Venturini, G. (2003, December). Interactive evolution of ant paintings. In The 2003 Congress on Evolutionary Computation, CEC’03. (Vol. 2, pp. 1376–1383). IEEE.

  Google Scholar 

• Bishop, J. M. (1989). Stochastic searching networks. In 1989 First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313) (pp. 329–331). IET.

  Google Scholar 

• Bisig, D., Schacher, J. C., Neukom, M. (2011). Flowspace-a hybrid ecosystem. In NIME (pp. 260–263).

  Google Scholar 

• Blackwell, T. (2003). Swarm music: Improvised music with multi-swarms. Artificial Intelligence and the Simulation of Behaviour, University of Wales, 10, 142–158.

  Google Scholar 

• Blackwell, T. (2007). Swarming and music. In Evolutionary Computer Music (pp. 194–217). Springer. https://doi.org/10.1007/978-1-84628-600-1_9.

• Blackwell, T. (2008). Swarm granulation. In The Art of Artificial Evolution (pp. 103–122). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-72877-1_5.

• Blackwell, T. M., & Bentley, P. (2002, May). Improvised music with swarms. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600) (Vol. 2, pp. 1462–1467). IEEE.

  Google Scholar 

• Boyd, J. E., Hushlak, G., & Jacob, C. J. (2004). SwarmArt: interactive art from swarm intelligence. In Proceedings of the 12th Annual ACM International Conference on Multimedia (pp. 628–635). ACM. https://doi.org/10.1145/1027527.1027674.

• Brownlee, J. (2012). Clever algorithms, nature-inspired programming recipes, open source book.

  Google Scholar 

• Braitenberg, V. (1986). Vehicles: Experiments in synthetic psychology. MIT press.

  Google Scholar 

• Burton, J. L., & Franks, N. R. (1985). The foraging ecology of the army ant Eciton rapax: an ergonomic enigma? Ecological Entomology, 10(2), 131–141.

  Google Scholar 

• Chan, M. T., Gorbet, R., Beesley, P., & Kulič, D. (2015). Curiosity-based learning algorithm for distributed interactive sculptural systems. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3435–3441). IEEE. https://doi.org/10.1109/IROS.2015.7353856.

• Chialvo, D. R., & Millonas, M. M. (1995). How swarms build cognitive maps. In The Biology and Technology of Intelligent Autonomous Agents (pp. 439–450). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-79629-6_20.

• Chu, X., Wu, T., Weir, J. D., Shi, Y., Niu, B., & Li, L. (2018). Learning-interaction-diversification framework for swarm intelligence optimizers: A unified perspective. Neural Computing and Applications, 1-21. https://doi.org/10.1007/s00521-018-3657-0.

• Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513–516.

  Article  Google Scholar 

• Delgado-Mata, C., Martinez, J. I., Bee, S., Ruiz-Rodarte, R., & Aylett, R. (2007). On the use of virtual animals with artificial fear in virtual environments. New Generation Computing, 25(2), 145–169. https://doi.org/10.1007/s00354-007-0009-5.

  Article  MATH  Google Scholar 

• Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66. https://doi.org/10.1109/4235.585892.

  Article  Google Scholar 

• Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26(1), 29–41. https://doi.org/10.1109/3477.484436.

  Article  Google Scholar 

• Dorigo, M., Maniezzo, V., & Colorni, A. (1991). The ant system: An autocatalytic optimizing process.

  Google Scholar 

• Dorigo, M. (1992). Optimization, learning and natural algorithms (PhD Thesis). Politecnico di Milano.

  Google Scholar 

• Fernandes, C., Mora, A. M., Merelo, J. J., Ramos, V., & Laredo, J. L. J. (2008a). KohonAnts: a self-organizing ant algorithm for clustering and pattern classification. arXiv preprint arXiv:0803.2695.

  Google Scholar 

• Fernandes, C., Mora, A. M., Merelo, J. J., Ramos, V., Laredo, J. L., & Rosa, A. (2008b). KANTS: Artifical ant system for classification. In International Conference on Ant Colony Optimization and Swarm Intelligence (pp. 339–346). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-87527-7_35.

• Fernandes, C. M. (2010). Pherographia: Drawing by ants. Leonardo, 43(2), 107–112. https://doi.org/10.1162/leon.2010.43.2.107.

  Article  Google Scholar 

• Fernandes, C. M., Mora, A., Merelo, J. J., & Rosa, A. C. (2012). Pherogenic Drawings-Generating Colored 2-dimensional Abstract Representations of Sleep EEG with the KANTS Algorithm. In International Conference on Evolutionary Computation Theory and Applications (Vol. 2, pp. 72–80). SCITEPRESS.

  Google Scholar 

• Fernandes, C. M., Mora, A. M., Merelo, J. J., & Rosa, A. C. (2015). Photorealistic rendering with an ant algorithm. In Computational Intelligence (pp. 63–77). Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_5.

• Gordon, D. (2010). Ant encounters: Interaction networks and colony behavior. Princeton University Press. https://doi.org/10.1515/9781400835447.

  Book  Google Scholar 

• Greenfield, G., & Machado, P. (2015). Ant-and ant-colony-inspired alife visual art. Artificial Life, 21(3), 293–306. https://doi.org/10.1162/ARTL_a_00170.

• Greenfield, G. (2005). Evolutionary methods for ant colony paintings. In Workshops on applications of evolutionary computation (pp. 478–487). Springer. https://doi.org/10.1007/978-3-540-32003-6_48.

• Greenfield, G. (2006a). Ant paintings using a multiple pheromone model. In R. Sarhangi & J. Sharp (Eds.), BRIDGES 2006 Conference Proceedings (pp. 319–326).

  Google Scholar 

• Greenfield, G. (2006b). On evolving multi-pheromone ant paintings. In 2006 IEEE World Congress
on Computational Intelligence. Vancouver: IEEE Press. WCCI06 Conference Proceedings (DVD-ROM ISBN: 0780394895) (pp. 7425–7431).

  Google Scholar 

• Greenfield, G. (2011). Abstract overlays using a transport network model. In Proceedings of Bridges 2011: Mathematics, Music, Art, Architecture, Culture (pp. 45–50). Tessellations Publishing.

  Google Scholar 

• Greenfield, G. (2012). Stigmmetry prints from patterns of circles. In Proceedings of Bridges 2012: Mathematics, Music, Art, Architecture, Culture (pp. 291–298). Tessellations Publishing.

  Google Scholar 

• Greenfield, G. (2013). Ant paintings based on the seed foraging behavior of P. barbatus. In 2013 BRIDGES Conference Proceedings.

  Google Scholar 

• Greenfield, G. (2013). On simulating seed foraging by red harvester ants. In 2013 IEEE Symposium on Artificial Life (ALife) (pp. 105–112). IEEE. https://doi.org/10.1109/ALIFE.2013.6602438.

• Greenfield, G., & Sarhangi, R. (2014). Target curves for pick-up, carry and drop mobile automata. In Bridges 2014 Conference Proceedings (pp. 381–384).

  Google Scholar 

• Guéret, C., Monmarché, N., & Slimane, M. (2004, September). Ants can play music. In International Workshop on Ant Colony Optimization and Swarm Intelligence (pp. 310–317). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28646-2_29.

• Hartman, C., & Benes, B. (2006). Autonomous boids. Computer Animation and Virtual Worlds, 17(3–4), 199–206. https://doi.org/10.1002/cav.123.

  Article  Google Scholar 

• Jacob, C. J., Hushlak, G., Boyd, J. E., Nuytten, P., Sayles, M., & Pilat, M. (2007). Swarmart: Interactive art from swarm intelligence. Leonardo, 40(3), 248–254. https://doi.org/10.1162/leon.2007.40.3.248.

  Article  Google Scholar 

• Jones, J. (2010). Characteristics of pattern formation and evolution in approximations of Physarum transport networks. Artificial Life, 16(2), 127–153. https://doi.org/10.1162/artl.2010.16.2.16202.

  Article  Google Scholar 

• Jones, D. (2008). AtomSwarm: A framework for swarm improvisation. In Workshops on Applications of Evolutionary Computation (pp. 423–432). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-78761-7_45.

• Kaplan, F. (2000). L’Émergence d’un Lexique dans une Population d’Agents Autonomes Ph.D. diss. University of Paris VI.

  Google Scholar 

• Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69. https://doi.org/10.1007/BF00337288.

• Machado, P., Martins, T., Amaro, H., Abreu, P. H., Martins, T., Machado, P., & Martins, T. (2013). Evolving stencils for typefaces: Combining machine learning, user’s preferences and novelty. Complexity, 49, 64–78.

  Google Scholar 

• Machado, P., & Pereira, L. (2012). Photogrowth: Non-photorealistic renderings through ant paintings. In Proceedings of the 14th annual conference on Genetic and evolutionary computation (pp. 233–240). ACM. https://doi.org/10.1145/2330163.2330197.

• Minsky, M. (1982). Music, mind, and meaning. In Music, mind, and brain (pp. 1-19). Springer. https://doi.org/10.1007/978-1-4684-8917-0_1.

• Mora, A. M., Fernandes, C. M., Herrera, L. J., Castillo, P. A., Merelo, J. J., Rojas, F., & Rosa, A. C. (2010). Sleeping with ants, SVMs, multilayer perceptrons and SOMs. In 2010 10th International Conference on Intelligent Systems Design and Applications (pp. 126–131). IEEE. https://doi.org/10.1109/ISDA.2010.5687278.

• Moura, L. (2002). Swarm paintings-non-human. ARCHITOPIA Book, Art, Architecture and Science 1–24.

  Google Scholar 

• Oudeyer, P. Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286. https://doi.org/10.1109/TEVC.2006.890271.

  Article  Google Scholar 

• Oudeyer, P. Y., Kaplan, F., Hafner, V. V., & Whyte, A. (2005). The playground experiment: Task-independent development of a curious robot. In Proceedings of the AAAI Spring Symposium on Developmental Robotics (pp. 42–47). California: Stanford.

  Google Scholar 

• Poli, R. (2008). Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications, 2008, 1–10. https://doi.org/10.1155/2008/685175.

  Article  Google Scholar 

• Ramos, V., & Almeida, F. (2004). Artificial ant colonies in digital image habitats-a mass behaviour effect study on pattern recognition. arXiv preprint cs/0412086.

  Google Scholar 

• Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. In SIGGRAPH ‘87 Conference Proceedings (Vol. 21, No. 4, pp. 25–34). Anaheim, CA: ACM Press. https://doi.org/10.1145/37401.37406.

• Sayles, M., Wu, X., & Boyd, J. E. (2003, January). Caml: Camera markup language for network interaction. In Internet Imaging IV. International Society for Optics and Photonics. (Vol. 5018, pp. 248–256). https://doi.org/10.1117/12.476176.

• Semet, Y., O’Reilly, U. M., & Durand, F. (2004). An interactive artificial ant approach to non-photorealistic rendering. In Genetic and Evolutionary Computation Conference (pp. 188–200). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24854-5_17.

• Shiffman, D. (2004). Swarm, ACM SIGGRAPH 2004 Emerging Technologies Proceedings (p. 26). New York: ACM Press. https://doi.org/10.1145/1186155.1186182.

• Todd, P. M., & Miranda, E. R. (2006). Putting some (artificial) life into models of musical creativity. In Musical Creativity (pp. 392–412). Psychology Press.

  Google Scholar 

• Tzafestas, E. S. (2000). Integrating drawing tools with behavioral modeling in digital painting. In Proceedings of the 2000 ACM Workshops on Multimedia (pp. 39–42). ACM. https://doi.org/10.1145/357744.357756.

• Unemi, T., & Bisig, D. (2005). Flocking Orchestra-to play a type of generative music by interaction between human and flocking agents. In Proceedings of the eighth Generative Art Conference (pp. 19–21).

  Google Scholar 

• Urbano, P. (2006). Consensual paintings. In Workshops on applications of evolutionary computation (pp. 622–632). Springer. https://doi.org/10.1007/11732242_59.

• Urbano, P. (2011). The T. albipennis sand painting artists. In European Conference on the Applications of Evolutionary Computation (pp. 414–423). Heidelberg: Springer, Berlin. https://doi.org/10.1007/978-3-642-20520-0_42.

• Urbano, P. (2005a). Playing in the pheromone playground: Experiences in swarm painting. In F. Rothlauf et al. (Eds.), Applications of Evolutionary Computing, EvoWorkshops 2005 Proceedings (pp. 478–487). Heidelberg: Springer. https://doi.org/10.1007/978-3-540-32003-6_53.

• Urbano, P. (2008). Swarm Exquisite-Corpses Games. In AAAI Spring Symposium: Creative Intelligent Systems (pp. 110–116).

  Google Scholar 

• von Mammen, S., & Jacob, C. (2007a, September). Genetic swarm grammar programming: Ecological breeding like a gardener. In 2007 IEEE Congress on Evolutionary Computation (pp. 851–858). IEEE. https://doi.org/10.1109/CEC.2007.4424559.

• von Mammen, S., & Jacob, C. (2007b). Swarm grammars: Growing dynamic structures in 3D agent spaces. Digital Creativity, 18(1), 54–64. https://doi.org/10.1080/14626260701253622.

  Article  Google Scholar 

• von Mammen, S., Wong, J., & Jacob, C. (2008, March). Virtual constructive swarm compositions and inspirations. In Workshops on Applications of Evolutionary Computation (pp. 491–496). Springer. https://doi.org/10.1007/978-3-540-78761-7_53.

• Walter, W. G. (1951). A machine that learns. Scientific American, 185(2), 60–64.

  Google Scholar 

• Weisstein, E. W. (2005). Moore neighborhood. From MathWorld-A Wolfram Web Resource. https://mathworld.wolfram.com/MooreNeighborhood.html.

• Wilson, E. O. (1971). The social insects. The Belknap Press of Harvard University Press.

  Google Scholar 

• Wilson, E. O., & Hölldobler, B. (1994). Journey to the ants: A story of scientific exploration. Belknap Press of Harvard University Press.

  Google Scholar 

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