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4th International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND)

作者： Fan, Dandan Qin, Haomiao Anyang Inst Technol Coll Art & Design Anyang 455000 Henan Peoples R China City Univ Macau Fac Business Macau 999078 Peoples R China

ISBN: (纸本)9783031243516;9783031243523

genetic interaction algorithm transforms the cognitive evaluation of the subject into scientific quantitative factors, and quantitative evaluation of the subject users by using the genetic function based on genetic algorithm. The aim of this work is to study the optimal design of the urban residential environment based on genetic interaction algorithms. This paper first uses OpenGL to create an urban residential environment model in order to evaluate the effect of the conceptual design of urban residential environment optimization using paired comparison methods. Then, we optimize the design of greening, infrastructure, air quality and sanitation in the creation of urban living environment. The experiment proved that the questionnaire survey results of 600 people showed that the residents' satisfaction with the interactive genetic algorithm scheme was much greater than the traditional scheme.

关键词： interactive genetic algorithm Living environment Environmental optimization Infrastructure

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Dynamic visualization design of visual symbols using interactive genetic algorithm

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SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Yin, Minji Qu, Huixue Zhang, Kailing Guangxi Univ Sci & Technol Sch Humanities Arts & Design Liuzhou 545001 Peoples R China Nanjing Vocat Inst Mechatron Technol Dept Informat Engn Nanjing 210000 Peoples R China

The dynamic visualization design of visual symbols is a complex task that requires finding a solution that meets user needs and has good visual effects. However, considering both the personalized needs of users and the dynamic visualization effect of design elements is a huge challenge. To address this challenge, this study proposes a dynamic visual symbol design method based on interactive genetic algorithm, which integrates real-time user participation mechanism and dynamic interactive feedback system to achieve intelligent and personalized design process. Compared with traditional genetic algorithms, this method introduces a dynamic fitness evaluation mechanism with direct user participation on the basis of traditional genetic algorithms. Users can evaluate the visual effects of individual populations in real time through a graphical interactive interface, and support realtime adjustment of parameters such as symbol form and motion trajectory. At the same time, it combines Bayesian probability models and Gaussian process proxy models to achieve dynamic capture and prediction of user preferences. The results show that the proposed method has a prediction accuracy of 89.1% and user satisfaction of 97.4% in dynamic visual symbol design, which is significantly improved compared to traditional genetic algorithms. This study provides new ideas for solving the dynamic design problem of multi-objective and high-dimensional visual symbols, which will help promote the development of the field of visual communication.

关键词： Interaction design interactive genetic algorithm Visual symbols Visualization Graph sequence

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Research on interface design based on user's mental model driven by interactive genetic algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2021年第1期17卷 42-51页

作者： Wei, Zhen Nie, Jinghuan East China Normal Univ Sch Design Shanghai 200062 Peoples R China Shanghai Univ Engn Sci Sch Art & Design Shanghai 201620 Peoples R China

This study proposes a computer interactive design method based on the analytic hierarchy process (AHP) and interactive genetic algorithm (IGA) to explore the mental model of user interface and promote the optimal design of interface. According to user interview and three ways of mining user mental model, this paper studies the design factors that affect user experience. IGA is used in the form of visual coding, and visual combination of users is obtained by iteration of user rating method. AHP is introduced to obtain design factors, and the user preference is obtained by sorting the weight value of factors, then the user mental model is formed and transformed into design elements. The research results show this method can avoid neglecting user needs in visual design, promote the design process, simplify the database of IGA, and improve the accuracy of user evaluation.

关键词： user mental model interactive genetic algorithm IGA analytic hierarchy process AHP interactive interface design

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Design of geometric flower pattern for clothing based on deep learning and interactive genetic algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2024年第1期33卷

作者： Cong, Xu Zhang, Wenjia Dalian Neusoft Univ Informat Sch Digital Arts & Design Dalian 116023 Liaoning Peoples R China DanDong Bejor Clothing Co Ltd Dandong 118000 Liaoning Peoples R China

Geometric and floral models are an important part of clothing and have been used for thousands of years. Although the styles of geometric models and flower models have undergone changes over the centuries, they are still one of the important factors of clothing patterns, the important carrier of aesthetics, and the manifestation of people's spiritual views and cultural needs. The development and application of digital printing technology have freed people from excessive dependence on sewing and embroidery processes. Therefore, while deeply studying the design of clothing patterns, this work sorted and analyzed the geometric flower models of clothing through interactive genetic algorithms, and optimized programming to enrich the models of clothing with geometric textures. The results showed that the deviation value of geometric flower pattern design was constantly decreasing, while the optimal strategy value was constantly increasing. The mean deviation value was 0.82, which was a decrease of 0.21 on the seventh day compared with the first day;the mean value of the optimal strategy value was 0.84, which was an increase of 0.19 on the seventh day compared with the first day. The visual effect and creativity of the clothing flower pattern design under the interactive genetic algorithm are better than the traditional flower pattern design, and the visual effect and creativity under the interactive genetic algorithm are 9% higher than the traditional one.

关键词： clothing geometry flower design pattern design deep learning interactive genetic algorithm

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Clothing Style Recommendation System based on interactive genetic algorithm

Clothing Style Recommendation System based on Interactive Ge...

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作者： Kai Sun Long Wu Jing Qi Jun-Tao Ding Yue Wang Jun Zhang School of Apparel&Art Design Xi'an Polytechnic University Shaanxi Research Center of Clothing Engineering and Technology Xi'an Polytechnic University Shaanxi "Four Subjects and One Union" Clothing Intelligent Design and Manufacturing Engineering Technology Research Center Wuhan Textile and Apparel Digital Engineering Technology Research Center

Given consumers' increasing demand for personalized recommendations of clothing styles,this study proposes a recommendation system based on an interactive genetic algorithm for women's *** interactive genetic algorithm as the core,the system interacts with users through the interface to obtain preference information in realtime,and then integrates the user's implicit decision factors,such as emotions and preferences,into the system through the adaptive function and uses the genetic algorithm to optimize the recommendation results to meet the personalized needs of *** can then be optimized based on user experience and feedback.

关键词： Clothing Style Recommendation interactive genetic algorithm Personalized Recommendation System User Preference Recommendation Satisfaction

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Pattern design and optimization of yarn-dyed plaid fabric using modified interactive genetic algorithm

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JOURNAL OF THE TEXTILE INSTITUTE 2020年第11期111卷 1652-1661页

作者： Zhang, Ning Pan, Ruru Wang, Lei Wu, Yang Gao, Weidong Jiangnan Univ Key Lab Ecotext Minist Educ Wuxi Jiangsu Peoples R China

With the advancing consumption level, it is difficult to shorten the product cycle and meet consumer's demands during the design process of yarn-dyed plaid fabric. In order to extract consumers' preferences and obtain timely feedbacks, a novel method using modified interactive genetic algorithm (IGA) is proposed for pattern design and optimization of yarn-dyed plaid fabric in this paper. Initially, the pattern of plaid fabric was encoded using the natural number code. The population was initialized based on common colour schemes. Then, survival of the fittest was performed as the selection operator to extinguish unsatisfactory patterns and preserve satisfactory patterns. Subsequently, common crossover operator and mutation operator, special mutation operator including addition and deletion were implemented to generate offspring. Moreover, users can manipulate individuals directly for convergence acceleration during the evolutionary process. Finally, the experiment was carried out by twenty-four users of different age groups and genders. Experimental results indicate that the application of IGA is feasible and efficient, which can supply design references to the designer.

关键词： Yarn-dyed plaid fabric pattern design and optimization interactive genetic algorithm subjective evaluation

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A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

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INTERNATIONAL JOURNAL OF INDUSTRIAL ERGONOMICS 2020年 76卷 102901-102901页

作者： Wang, Tianxiong Zhou, Meiyu East China Univ Sci & Technol Sch Art Design & Media 130 Meilong Rd Shanghai 200237 Peoples R China

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.

关键词： interactive genetic algorithm Customer demand Fuzzy kano model User fatigue User preference

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interactive genetic algorithm based on typical style for clothing customization

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JOURNAL OF ENGINEERED FIBERS AND FABRICS 2020年第15期15卷

作者： Zhu, Xinjuan Li, Xuefei Chen, Yifan Liu, Jingwei Zhao, Xueqing Wu, Xiaojun Xian Polytech Univ Sch Comp Sci Xian 710048 Peoples R China Xian Polytech Univ Shaanxi Key Lab Clothing Intelligence Sch Comp Sci Xian Peoples R China Xian Polytech Univ Sch Apparel & Art Design Xian Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

Companies find it extremely difficult to predict consumers' needs and requirements, since the spiritual significance of clothing is getting more and more attention. However, most current clothing customization platforms only allow customers to retrieve previous design components from the database and recombine them together, ignoring the customer's personalized design requirements. In view of the above issues, an intelligent design approach of personalized customized clothing based on typical style and interactive genetic algorithm is proposed in this article. It could generate new fashion styles according to simple customer evolution. The binary coding scheme of suit coat style is presented. And an automatic suit coat design system based on interactive genetic algorithm is developed, in which 10 typical suit coats are selected as the initial population. The experimental results show that the system can alleviate customers' fatigue and speed up convergence compared with the classic interactive genetic algorithm design, and the designed styles can better meet customers' preferences.

关键词： Customized clothing design interactive genetic algorithm typical style automatic suit coat design system

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User Preference Adaptive Fitness of interactive genetic algorithm Based Ceramic Disk Pattern Generation Method

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IEEE ACCESS 2020年 8卷 95978-95986页

作者： Cai, Huafei Guangdong Univ Finance & Econ Sch Art & Design Guangzhou 510320 Peoples R China

Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm & x2019;s convergence speed and shows better evolutionary optimization ability.

关键词： genetic algorithms Ceramics Sociology Statistics Optimization Adaptation models Uncertainty interactive genetic algorithm user preferences fitness ceramic design of disc

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Pattern retrieval of yarn-dyed plaid fabric based on modified interactive genetic algorithm

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COLOR RESEARCH AND APPLICATION 2020年第6期45卷 1143-1152页

作者： Zhang, Ning Pan, Ruru Wang, Lei Meng, Shuo Gao, Weidong Jiangnan Univ Minist Educ Key Lab Ecotext Wuxi Peoples R China

With the development of perceptual consumption, consumers sometimes cannot explicitly describe the purchase demands or only based on impression, like the color perception and aesthetic experience. Based on the consumer's expression, it is difficult to design a new fabric by repeated proofing to meet the consumer's demands. To retrieve the existing patterns incorporating human intuition and emotion, this study proposed a novel pattern retrieval method of yarn-dyed plaid fabric using modified interactive genetic algorithm. Each pattern was encoded based on the design elements and visual features were extracted to bridge the semantic gap between the designer and the consumer. Survival of the fittest and two special mutation operators, addition and deletion, were designed to increase the diversity of the generations. During the evolution, the generated patterns were replaced by the most similar patterns in the database based on visual features. Experimental results showed that the proposed scheme is feasible and effective to extract the consumer's preferences and retrieve satisfactory patterns, helping the factory obtain the process sheet to guide production and save labor and material resources.

关键词： color appearance interactive genetic algorithm pattern retrieval perception consumption yarn-dyed plaid fabric

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