A Proposal for Distributed Interactive Genetic Algorithm for Composition of Musical Melody

  • Makoto Fukumoto Fukuoka Institute of Technology
  • Takeshi Hatanaka Fukuoka Institute of Technology
Keywords: : Interactive Genetic Algorithm, Multiple Users, Composition, Music Melody

Abstract

This study proposes an Interactive Evolutionary Computation that creates sound contents for multiple users. Sound contents including music piece and sign sound are often used for creating common atmosphere and transmitting a certain message to everyone. The proposed method is based on parallel distributed Interactive Genetic Algorithm (IGA). As a special property of this method, in some generations, solution candidates are exchanged between the users. With the exchange, each of the users is affected by other users’ feelings: good solutions for all of the users are expected to be obtained. Based on the proposed method, we constructed an IGA system for fundamentally investigating efficiencies of the proposed method. Aim of the IGA system is to create a short music melody commonly affording bright image to multiple users. Key of the notes was treated as gene of the IGA. Music chord progression is attached to the melody when it is presented to the subjects. In listening experiment, sixteen subjects divided into eight pairs, and two subjects participated in the evaluation process simultaneously. Experimental results showed increase in mean fitness values between the subjects.

Author Biographies

Makoto Fukumoto, Fukuoka Institute of Technology

Professor

Department of Computer Science and Engineering

Takeshi Hatanaka, Fukuoka Institute of Technology

T. Hatanaka already graduated from Fukuoka Inst. Tech. So e-mail written avobe may does not work now.

(Comment from M. Fukumoto)

References

H. Takagi, “Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation,” Proc. the IEEE, vol.89, no.9, 2001, pp. 1275-1296.

R. Dawkins, The Blind Watchmaker, Norton, 1986.

M. Herdy, “Evolutionary optimization based on subjective selection -evolving blends of coffee,” Proc. 5th European Congress on Intelligent Techniques and Soft Computing, 1997, pp. 640-644.

M. Fukumoto and J. Imai, “Design of Scents Suited with User’s Kansei using Interactive Evolutionary Computation,” Proc. Kansei Engineering and Emotion Research 2010, 2010, pp. 1016-1022.

M. Fukumoto, M. Inoue, and J. Imai, “User’s Favorite Scent Design Using Paired Comparison-based Interactive Differential Evolution,” Proc. IEEE CEC2010, 2010, pp. 4519-4524.

M. Fukumoto, K. Kawai, M. Inoue, and J. Imai, “Interactive Tabu Search with Paired Comparison for Optimizing Fragrance,” Proc. IEEE SMC2013, 2013, pp. 1690-1695.

M. Fukumoto, S. Koga, M. Inoue, and J. Imai, “Interactive Differential Evolution Using Time Information Required for User’s Selection: In A Case of Optimizing Fragrance Composition,” Proc. IEEE CEC2015, 2015, pp. 2192-2198.

H. Nishino, K. Takekata, M. Sakamoto, B. A. Salzman, T. Kagawa, and K. Utsumiya, “An IEC-Based Haptic Rendering Optimizer,” Proc. IEEE WSTST’05, 2005, pp. 653-662.

K. Takekata, B. A. Salzman, H. Nishino, T. Kagawa, and K. Utsumiya, “An Intuitive Optimazation Method of Haptic Rendering Using Interactive Evolutionary Computation,” Proc. IEEE SMC2005, 2005, pp. 1896-1901.

M. Fukumoto and T. Ienaga, “A Proposal for Optimization Method of Vibration Pattern of Mobile Device with Interactive Genetic Algorithm,” Proc. HCII2013, vol.11, 2013, pp. 264-269.

R. Akase and Y. Okada, “Web-based Multiuser 3D Room Layout System Using Interactive Evolutionary Computation with Conjoint Analysis,” Proc. of the 7th International Symposium on Visual Information Communication and Interaction, 2014, pp. 178-187.

H. Takenouchi, M. Tokumaru, and N. Muranaka, “An interactive genetic algorithm with tournament evaluation of individuals by multiple people,” Proc. SCIS&ISIS2008, 2008, pp. 1061-1066.

H. Takenouchi, H. Inoue, and M. Tokumaru, “Signboard design system through social voting technique,” Proc. ISIC2014, 2014, pp. 14-19.

M. Sakai, H. Takenouchi, and M. Tokumaru, “Design support system with votes from multiple people using digital signage,” Proc. ISIC2014, 2014, pp. 26-31.

Y. Ogawa, M. Miki, T. Hiroyasu, and Y. Nagaya, “A New Collaborative Design Method Based on Interactive Genetic Algorithms,” eurogen, 2001.

M. Miki, Y. Yamamoto, S. Wake, and T. Hiroyasu, “Global Asynchronous Distributed Interactive Genetic Algorithm,” Proc. IEEE SMC2006, vol.4, 2006, pp. 3481-3485.

J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. The University of Michigan Press, USA, 1975.

D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Professional, USA, 1989.

M. Fukumoto and T. Hatanaka, “Parallel Distributed Interactive Genetic Algorithm for Composing Music Melody Suited to Multiple Users’ Feelings,” Proc. ICIS2016, 2016, pp. 831-836.

M. Miki, H. Orita, S. H. Wake, and T. Hiroyasu, “Design of Sign Sounds using an Interactive Genetic Algorithm,” Proc. the IEEE SMC2006, vol.4, 2006, pp. 3486-3490.

M. Fukumoto, T. Hazama, and J. Imai, “Evolutionary Computation for Creating Musical Melody based on User’s Physiological Index,” Proc. SCIS&ISIS2008, 2008, pp. 247-252.

M. Fukumoto, “An Efficiency of Interactive Differential Evolution for Optimization of Warning Sound with Reflecting Individual Preference,” IEEJ Transactions on Electrical and Electronic Engineering, vol.10, issue S1, 2015, pp. S77–S82.

C. E. Osgood, G. J. Suci, and P. Tannenbaum, The measurement of meaning, University of Illinois Press, 1957

Published
2017-06-30
Section
Technical Papers (Information and Communication Technology)