User's favorite scent design using paired comparison-based Interactive Differential Evolution

This study proposes a method that creates a scent suited with a user's favor using paired comparison-based Interactive Differential Evolution. In the proposed method, the user smells two scents and selects the preferred one by simple comparison. Based on the repetitive comparisons, Differential Evolution (DE) optimizes the scent suited with the user. Each scent is composed of several source scents, and strength of each source scent is described as values in DE's vector. To investigate the efficacy of the proposed method fundamentally, smelling experiments composed of comparing experiment and evaluating experiment are performed. In the comparing experiment, the subjects compare presented pair scents and select the preferred one through ten generations, and DE evolves scent to user's favor based on the comparisons. In the evaluating experiment, the subjects evaluate four representative scents picked from 0-, 3-, 6-, 9-th generations, respectively. The results of the experiments showed a tendency of the increase of fitness value in accordance with evolution.

[1]  A. Sima Etaner-Uyar,et al.  Active Appearance Model-Based Facial Composite Generation with Interactive Nature-Inspired Heuristics , 2006, MRCS.

[2]  Godfrey C. Onwubolu,et al.  Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization , 2009 .

[3]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[6]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[7]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[8]  Denis Pallez,et al.  Paired Comparisons-based Interactive Differential Evolution , 2009, ArXiv.

[9]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  Saku Kukkonen,et al.  Real-parameter optimization with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  Makoto Fukumoto,et al.  DESIGN OF SCENTS SUITED WITH USER'S KANSEI USING INTERACTIVE EVOLUTIONARY COMPUTATION , 2010 .

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[15]  Karl-Dirk Kammeyer,et al.  Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  C. Osgood,et al.  The Measurement of Meaning , 1958 .

[17]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[18]  Hitoshi Iba,et al.  Accelerating Differential Evolution Using an Adaptive Local Search , 2008, IEEE Transactions on Evolutionary Computation.