A proposal for optimization of sign sound using interactive differential evolution.
ABSTRACT Interactive Evolutionary Computation (IEC) is known as an effective method to create media contents suited to user's preference and objectives to use. The present study proposed an IEC method that creates sign sounds with interactive differential evolution. In the user's evaluation, paired comparison was employed: the user selects better one from two presented media contents instead of scoring many solution candidates in a same time. To fundamentally investigate the efficacy of the proposed method, two listening experiments were performed; experiment 1 as comparing experiment and experiment 2 as re-evaluating experiment. Target of the creation with the proposed method was warning sign sounds. Eight males participated as subjects in both experiment. In the result of the experiment 1, gradual decrease of total Euclidean distance between eight DE's vectors in each generation was observed. In the result of the experiment 2, gradual increase of the subjective fitness value was observed. These results were not significant, however, shrink of searching space and increase of fitness value suggested a possibility of the proposed method to create sign sounds suited to the user.
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Conference Paper: Paired comparison-based Interactive Differential Evolution[Show abstract] [Hide abstract]
ABSTRACT: We propose a system of Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are central to reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals with each other but rather only to compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed method is superior to others from both user interface and convergence performance points of view.Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on; 01/2010
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ABSTRACT: We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papersProceedings of the IEEE 10/2001; · 5.47 Impact Factor
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ABSTRACT: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.IEEE Transactions on Evolutionary Computation 03/2011; · 5.55 Impact Factor