Design of Sign Sounds using an Interactive Genetic Algorithm

Miki, Mitsunori; Orita, Hiroko; Wake, Sanae H.; Hiroyasu, Tomoyuki

doi:10.1109/icsmc.2006.384659

Cited by 42 publications

(28 citation statements)

References 6 publications

(5 reference statements)

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“…Inoue et al, Kinoshita et [3][4][5][6]. The advantage of IEC is the incorporation of human evaluations into the results.…”

Section: Iec-based Color Support Methodsmentioning

confidence: 99%

“…Existing IEC-based color schemes generating systems provide users with several suggested color schemes [1,2]. Other existing systems support color design for clothing, interior accessories, and products [3][4][5][6]. An IEC-based approach gives users various color scheme candidates by changing color patterns; hence, users can find their favorites among all candidates.…”

Section: Introductionmentioning

confidence: 99%

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Statistics-Based Interactive Evolutionary Computation for Color Scheme Search

Ishibashi

2015

IJAE

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This paper presents a statistics-based interactive evolutionary computation (IEC) method for color scheme searches. Generally, people who do not have sufficient skill or knowledge of colors need to devote considerable time and effort to creating color schemes. To overcome this problem, we propose a statistics-based interactive genetic algorithm and four similarity search functions. This combination allows users to easily and effectively obtain appropriate color schemes. To investigate the performance of the proposed method, we conducted two experiments and confirmed that the implemented application of the proposed method allows users to obtain a desired color scheme in less than 48 s. We also confirmed that the proposed method could provide users favorable recolored illustrations in less than 52 s. In addition, we confirmed that the search ability of the proposed method is superior to that of the conventional IEC method through a comparison experiment.

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“…Inoue et al, Kinoshita et [3][4][5][6]. The advantage of IEC is the incorporation of human evaluations into the results.…”

Section: Iec-based Color Support Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistics-Based Interactive Evolutionary Computation for Color Scheme Search

Ishibashi

2015

IJAE

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“…In this study, the efficiency of the proposed IEC is investigated by using an IEC system creating sign sounds. Some previous studies proposed IEC that searches sign sounds by employing Genetic Algorithm and Differential Evolution (DE) [9,10].…”

Section: Interactive Evolutionary Computation System Creating Sign Soundmentioning

confidence: 99%

A Proposal for Continuous Evaluation-based Interactive Evolutionary Computation

Fukumoto

Hanada

2017

Stochastic Systems Theory and its Applications (SSS)

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Interactive Evolutionary Computation (IEC) is known as an efficient method which reflects user's subjective preference on media contents. However, user's fatigue caused by repetitive evaluation tasks remains as a severe problem in IEC. The purpose of this study is to propose a new framework of IEC, continuous evaluation-based IEC (CEIEC) that continues user's evaluation tasks in many days. CEIEC allows the user's subjective evaluation day by day to have many evaluation times without severe fatigue, while conventional IECs performed user's task in one time in one day. This study also performed a listening experiment for investigating efficiencies of CEIEC. The target of search with CEIEC was to design a bright sign sound. Sixteen subjects participated in the listening experiment which is composed of tasks of three days: in the 1st and the 2nd days, the subjects performed a selection task throughout fifteen generations. Successive update of the solution was observed during these two days. In the 3rd day, the subjects evaluated three sign sounds with 7-point scale. These three sounds were picked up from the 0th, 14th, and 29th generations, respectively. The sound with the highest score was found in the 29th generation. These results show the efficiencies of CEIEC.

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“…The Interactive Genetic Algorithm (IGA) is known as an IEC method. Researchers have studied various IEC systems, including image-retrieval systems [2], clothing-design systems [3], interior-layout systems [4], sign-sound-generation systems [5], hearing-aidfitting systems [6], and robot-action-generation systems [7]. These studies used various methods to help users evaluate product solutions.…”

Section: Introductionmentioning

confidence: 99%

Interactive Evolutionary Computation Using a Tabu Search Algorithm

Takenouchi

Tokumaru

Muranaka

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe present an Interactive Tabu Search (ITS) algorithm to reduce the evaluation load of Interactive Evolutionary Computation (IEC) users. Most previous IEC studies used an evaluation interface that required users to provide evaluation values for all candidate solutions. However, user's burden with such an evaluation interface is large. Therefore, we propose ITS where users choose the favorite candidate solution from the presented candidate solutions. Tabu Search (TS) is recognized as an optimization technique. ITS evaluation is simpler than Interactive Genetic Algorithm (IGA) evaluation, in which users provide evaluation values for all candidate solutions. Therefore, ITS is effective for reducing user evaluation load. We evaluated the performance of our proposed ITS and a Normal IGA (NIGA), which is a conventional 10-stage evaluation, using a numerical simulation with an evaluation agent that imitates human preferences (Kansei). In addition, we implemented an ITS evaluation for a runningshoes-design system and examined its effectiveness through an experiment with real users. The simulation results showed that the evolution performance of ITS is better than that of NIGA. In addition, we conducted an evaluation experiment with 21 subjects in their 20 s to assess the effectiveness of these methods. The results showed that the satisfaction levels for the candidates generated by ITS and NIGA were approximately equal. Moreover, it was easier for test subjects to evaluate candidate solutions with ITS than with NIGA. key words: interactive evolutionary computation, user interface, interactive tabu search

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Design of Sign Sounds using an Interactive Genetic Algorithm

Cited by 42 publications

References 6 publications

Statistics-Based Interactive Evolutionary Computation for Color Scheme Search

Statistics-Based Interactive Evolutionary Computation for Color Scheme Search

A Proposal for Continuous Evaluation-based Interactive Evolutionary Computation

Interactive Evolutionary Computation Using a Tabu Search Algorithm

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