Discussion of Offspring Generation Method for Interactive Genetic Algorithms with Consideration of Multimodal Preference

Ito, Fuyuko; Hiroyasu, Tomoyuki; Miki, Mitsunori; Yokouchi, Hisatake

doi:10.1007/978-3-540-89694-4_36

Cited by 13 publications

(14 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work is close to the works developed by Ito et al [10], Llora et al [12] and Kagawa et al [11]. Ito et al [10] considered multimodal preference within IGA but did not considered neither dependencies among design variables nor fitness modeling during evolution. Llora et al [12] used support vector machines to synthesize a fitness model based on user evaluation.…”

Section: Related Worksupporting

confidence: 76%

“…But, the user has to evaluate each individual in every generation. For example, if IGA evolves 20 generations with populations of 20 individuals (as it is proposed in their paper [10]), the user should carry out 400 evaluations. Then, the bad point is given by the human tedium and fatigue caused in the evaluation process.…”

Section: Finding the Optimal Image With Igamentioning

confidence: 99%

“…IGA has been applied to predict a user's preference based on his subjective evaluation [10]. Authors proposed a new offspring generation method introducing clustering of selected individuals and generating offspring from each clusters.…”

Section: Finding the Optimal Image With Igamentioning

confidence: 99%

“…On the other hand, user's preferences are unimodal or multimodal (i.e. guided by more than one criteria) [10]. The paper in [10] searches multimodal user preferences by introducing a clustering technique (each cluster represents one user criteria) and generating offspring from each cluster.…”

Section: Introductionmentioning

confidence: 99%

“…guided by more than one criteria) [10]. The paper in [10] searches multimodal user preferences by introducing a clustering technique (each cluster represents one user criteria) and generating offspring from each cluster. Here, we show a system that searches customer's preference according to colors of a sample image.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Interactive Genetic Algorithm with fitness modeling for the development of a color simulation system based on customer’s preference

Rodriguez

Diago

Hagiwara

2011

Japan J. Indust. Appl. Math.

View full text Add to dashboard Cite

Customers preferences are difficult to predict automatically because they are subjective and relative to human likeness patterns which are different from each people. This paper proposes the combination of Interactive Genetic Algorithm (IGA) with Artificial Neural Networks to model the fitness function and find the optimally colored image according to user's preference. As the search space for the IGA is very large, proposed system reduces it creating genotypes with colors restrictions developed with a new graph coloring technique. Two functions are proposed for coding color restrictions. The system presented in this study takes the case of the color design of a building for illustration purposes. Experimental results show that 80% reduction in the number of users evaluations is achieved with proposed combination and speedup in IGA evolution is achieved when color restrictions are coded by proposed mutation and crossover operators. Mathematics Subject Classification (2000)68T20 · 68T05 · 68U10 · 68U20 · 90C35 · 90C59 · 91B42 · 92B20

show abstract

Section: Related Worksupporting

confidence: 76%

Section: Finding the Optimal Image With Igamentioning

confidence: 99%

Section: Finding the Optimal Image With Igamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interactive Genetic Algorithm with fitness modeling for the development of a color simulation system based on customer’s preference

Rodriguez

Diago

Hagiwara

2011

Japan J. Indust. Appl. Math.

View full text Add to dashboard Cite

show abstract

A Soft-Computing Approach for Quantification of Personal Perceptions

Diago

Romero

Shinoda

et al. 2016

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

I see, you design: user interface intelligent design system with eye tracking and interactive genetic algorithm

Cheng

Dey

2019

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

User interfaces are important for streamlining the interactions between humans and computers. However, there are few effective approaches for collecting users' preferences implicitly and objectively for the purpose of user interface (UI) design optimization. This paper presents an effective approach to interactive genetic algorithm (IGA) optimization-based UI design via eye tracking, including eye-movement data based users' preference inferring, gene coding for real UI components and design features, and the visualization and interaction mechanisms. Then we design and build a prototype system that applies IGA to generate and optimize music player UI solutions automatically. An evaluation of our prototype system suggests that it can generate and identify personalized UI designs reliably with minimal user intervention and high efficiency and user satisfaction.

show abstract

Discussion of Offspring Generation Method for Interactive Genetic Algorithms with Consideration of Multimodal Preference

Cited by 13 publications

References 4 publications

Interactive Genetic Algorithm with fitness modeling for the development of a color simulation system based on customer’s preference

Interactive Genetic Algorithm with fitness modeling for the development of a color simulation system based on customer’s preference

A Soft-Computing Approach for Quantification of Personal Perceptions

I see, you design: user interface intelligent design system with eye tracking and interactive genetic algorithm

Contact Info

Product

Resources

About