Interactive Evolutionary Computation Creating Congruent Media Content Composed of Different Media Types
Abstract
We use multiple media content every day, and using congruent media content composed of different media types is ideal for users. However, it is still difficult to obtain congruent media content. Interactive Evolutionary Computation (IEC) is a well-known method for obtaining good media content suited to each user’s feelings as solutions to search problems. Conventional IECs were used for searching sole media type. This study proposes a new IEC that searches the congruent media content as a good combination of different types of media content. In the proposed IEC, the solution candidate contains variables corresponding to different media types. A system was constructed with a genetic algorithm, and it was used to investigate the efficiencies of the pro-posed IEC in the experiment. The target of creation was a relaxing set of music melody and scent. Twenty participants evaluated sets of music melodies and scents throughout ten generations in the search experiment. The experimental results showed a significant increase in the mean fitness and a significant decrease in the distance between solutions. No significant increase was observed in the maximum fitness values.
References
R. Terlutter, S. Diehl, I. Koinig, and M.K.J. Waiguny, “Positive or Negative Effects of Tech-nology Enhancement for Brand Placements? Memory of Brand Placements in 2D, 3D, and 4D Movies,” Media Psychology, vol. 19, no. 4, 2016, pp. 505-533.
D.Y. Jeong, S.H. Han, S. Choi, and D. Jeong, “Investigating perceived emotions and affects of a scene, and the user satisfaction with motion effects in 4D movies,” Int’l J. of Industrial Ergonomics, vol. 85, no. 2, 2021, 103173.
H. McGurk and J. MacDonald, “Hearing lips and seeing voices,” Nature, vol. 264, pp. 746-748, 1976.
A. Crisinel and C. Spence, “A Fruity Note: Crossmodal associations between odors and mu-sical notes,” Chemical Senses, vol. 37, no. 2, pp. 151-158, 2012.
O. Deroy, A. Crisinel, and C. Spence, “Crossmodal correspondences between odors and con-tingent features: odors, musical notes, and geometrical shapes,” Psychonomic Bulletin & Re-view, vol. 20, no. 5, 2013, pp. 878-896.
T. Wakata and M. Saito, “A study of common dimensions on the impression of color, fra-grance and music,” J. of the Color Science Association of Japan, vol. 41, no. 3, 2017, pp. 54-57.
M. Fukumoto, “Investigation of Main Effect of Scent in Cross-modal Association between Music and Scent,” Int’l J. of Affective Engineering, vol. 19, no. 4, 2020, pp.259-264.
C. Spence, “Musical Scents: On the Surprising Absence of Scented Musical/Auditory Events, Entertainments, and Experiences,” i-Perception, vol. 12, no. 5, 2021, pp. 1-31.
R. Dawkins, The Blind Watchmaker, Norton, UK, 1986.
H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Opti-mization and Human Evaluation,” Proc. the IEEE, vol. 89, no. 9, 2001, pp. 1275-1296.
M. Fukumoto and J. Imai, “Design of Scents Suited with User’s Kansei using Interactive Evolutionary Computation,” Proc. Int’l Conf. on Kansei Engineering and Emotion Research 2010, 2010, pp.1016-1022.
H. Nishino, K. Takekata, M. Sakamoto, B. A. Salzman, T. Kagawa, and K. Utsumiya, “An IEC-Based Haptic Rendering Optimizer,” Proc. the IEEE Int’l Workshop on Soft Computing as Transdisciplinary Science and Technology 2005, 2005, pp. 653-662.
M. Herdy, “Evolutionary optimization based on subjective selection -evolving blends of coffee,” in Proc. 5th European Congress on Intelligent Techniques and Soft Computing, 1997, pp. 640-644.
M. Fukumoto and Y. Hanada, “A Proposal for Creation of Beverage Suited for User by Blending Juices based on Interactive Genetic Algorithm,” Proc. IEEE Systems, Man, and Cy-bernetics 2019, 2019, pp. 1104-1109.
M. Miki, H. Orita, S.H. Wake, and T. Hiroyasu, “Design of Sign Sounds using an Interactive Genetic Algorithm”, Proc. the IEEE Int’l Conf. Systems, Man, & Cybernetics 2006, vol.4, 2006, pp.3486-3490.
M. Fukumoto and Y. Hanada, “Investigation of the efficiency of continuous evaluation-based interactive evolutionary computation for composing melody,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 15, no. 2, 2020, pp. 235-241.
J.H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Ann Arbor, MI: The University of Michigan Press, 1975.
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Read-ing, MA: Addison-Wesley Professional, 1989.
C.E. Osgood, G.K. Suci, and P. Tannenbaum, The measurement of meaning, University of Illinois Press, USA, 1957.
RYOUN web site; http://ryo-un.co.jp/information/business_2.html.
P. Zhang, A. Mitani, and M. Miyazaki, “Development of Control System for Blending and Generating of Scent,” Proc. of Int’l Conf. on Kansei Engineering and Emotion Research 2007, 2007, L-18.
H. Funato, M. Yoshikawa, M. Kawasumi, S. Yamamoto, M. Yamada, and Y. Yanagida, “Stimulation effects provided to drivers by fragrance presentation considering olfactory ad-aptation,” Proc. of 2009 IEEE Intelligent Vehicles Symposium, 2009, pp. 881-886.
J.P. McGann, “Poor human olfaction is a 19th-century myth,” Science, 356(6338), 2017, eaam7263.
C. Bushdid, M.O. Magnasco, L.B. Vosshall, and A. Keller, “Humans Can Discriminate More than 1 Trillion Olfactory Stimuli,” Science, 343(6177), 2014, pp. 1370-1372.
H. Takagi, “Interactive evolutionary computation for analyzing human awareness mecha-nisms,” Applied Computational Intelligence and Soft Computing, vol. 2012, Article ID 694836, 2012.
