Interactive Genetic Algorithm Research Articles

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Reference Image Aided Color Matching Design Based on Interactive Genetic Algorithm

This paper proposes an interactive genetic algorithm to assist designers in color selection and match different color combinations extracted. Then, based on the CorelDraw development environment, this paper establishes a graphic-based man-machine dialogue model. Through simulation experiments, genetic algorithms such as random selection, hybridization, variation, color region adjustment, etc. Finally, the best color combination is obtained and combined with the image. Within the scope of color extraction value, designers can use interactive genetic algorithm rules to view color planning from a holistic perspective. Under the influence of human-computer interaction, each scheme is optimized step by step, so that the color ideas matching the designer can be found quickly.

EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms

Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically Nondominated Sorting Genetic Algorithm-II (NSGA-II), which tries to maximize the yield and minimize the risk, simultaneously. The system, named EvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. EvoFolio is an interactive genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how EvoFolio can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue that EvoFolio can be effectively used by investors as a support tool for portfolio formation.

Acoustical and behavioral heuristics for fast interactive sound design.

During their creative process, designers routinely seek the feedback of end users. Yet, the collection of perceptual judgments is costly and time-consuming, since it involves repeated exposure to the designed object under elementary variations. Thus, considering the practical limits of working with human subjects, randomized protocols in interactive sound design face the risk of inefficiency, in the sense of collecting mostly uninformative judgments. This risk is all the more severe that the initial search space of design variations is vast. In this paper, we propose heuristics for reducing the design space considered during an interactive optimization process. These heuristics operate by using an approximation model, called surrogate model, of the perceptual quantity of interest. As an application, we investigate the design of pleasant and detectable electric vehicle sounds using an interactive genetic algorithm. We compare two types of surrogate models for this task, one based on acoustical descriptors gathered from the literature and the other based on behavioral data. We find that reducing by a factor of up to 64 an original design space of 4096 possible settings with the proposed heuristics reduces the number of iterations of the design process by up to 2 to reach the same performance. The behavioral approach leads to the best improvement of the explored designs overall, while the acoustical approach requires an appropriate choice of acoustical descriptor to be effective. Our approach accelerates the convergence of interactive design. As such, it is particularly suitable to tasks in which exhaustive search is prohibitively slow or expensive.

Multiobjective combinatorial optimization with interactive evolutionary algorithms: The case of facility location problems

Multiobjective combinatorial optimization with interactive evolutionary algorithms: The case of facility location problems

Interactive Evolutionary Computation Creating Congruent Media Content Composed of Different Media Types

Interactive Evolutionary Computation Creating Congruent Media Content Composed of Different Media Types

Exploring Graphic Design and its Interactivity in the Context of Cognitive Psychology

Abstract Cognitive psychology can help better understand human thinking and behavior, and its application in design can increase the infectious force of design. In this paper, because the brain will generate ERP signals when cognitive stimuli stimulate it, the group sparse decomposition method obtains ERP from EEG signals, and uses the BAP of brain regions to construct the interaction activity pattern of brain regions, and then combines with the interaction genetic algorithm to search for the optimal solution for the data in graphic design by using hierarchical degradation method for processing. The EEG data and interaction behavior characteristics of users were explored through chromatic stimulus response experiments and interaction behavior analysis. It was found that the accuracy of users’ responses to orange, green and purple were all higher than 60%, and P400 in EEG responded to the stimuli most significantly, with P-values of 0.0291, 0.0462, and 0.0355 at P5, P6, and Pz, respectively, with significant effects. The average number of interactions of 46 users in the graphic design enjoyment was 31.47, with the highest success rate of interaction reaching 99.81%, and the average duration of interaction experience for all subjects was 10.26 minutes. Graphic design based on cognitive psychology can capture people’s inner emotions and make better design solutions.

A Bilevel Periodically Interactive Evolutionary Algorithm for Personalized Service Customization in Wireless-Powered Cooperative MEC

A Bilevel Periodically Interactive Evolutionary Algorithm for Personalized Service Customization in Wireless-Powered Cooperative MEC

Creative collaboration with interactive evolutionary algorithms: a reflective exploratory design study

Progress in AI has brought new approaches for designing products via co-creative human–computer interaction. In architecture, interior design, and industrial design, computational methods such as evolutionary algorithms support the designer’s creative process by revealing populations of computer-generated design solutions in a parametric design space. Because the benefits and shortcomings of such algorithms’ use in design processes are not yet fully understood, the authors studied the intricate interactions of an industrial designer employing an interactive evolutionary algorithm for a non-trivial creative product design task. In an in-depth report on the in-situ longitudinal experiences arising between the algorithm, human designer, and environment, from ideation to fabrication, they reflect on the algorithm’s role in inspiring design, its relationship to fixation, and the stages of the creative process in which it yielded perceived value. The paper concludes with proposals for future research into co-creative AI in design exploration and creative practice.

Engineering equipment conceptual design methodology: using function units configuration and schemes iterative solution

Engineering equipment can efficiently assist humans in completing engineering projects and reduce work intensity. As the difficulty and scope of operations increase, higher requirements are placed on function and integration. In product design, excessive user requirements and design constraints lead to discrete results, difficult to converge. Under multi-functionality and high integration constraints, traditional mapping sub-functions to structures one by one results in overabundant mappings, and discrete, redundant structures after mapping, poorly achieving integration and reducing subsequent iteration efficiency. To address these issues, this paper proposes a conceptual design methodology for engineering equipment with function units configuration and schemes iterative solution. First, the Fuzzy Quality Function Deployment (FQFD) method applies fuzzy theory to requirement-to-function conversion, overcoming ambiguity and subjectivity of user requirements. Second, with the goal of integrated structures, an improved FBS method, i.e. Function-Function Units-Structure (FFUS), aggregates sub-functions after decomposition into function units, and then maps to structures, improving engineering equipment integration. Moreover, the Interactive Genetic Algorithm (IGA) method involves users in an interactive process to generate an optimised design scheme, overcoming blind solution generation. Prototype design and simulation of 8VN1 Gas Insulated Switchgear (GIS) handing equipment demonstrate that the methodologies are effective in aiding engineering equipment conceptual design.

Non‐allelic recombination interactive genetic algorithms for colour imagery reproduction in colour scheming

AbstractUsing image extracted colours as the colour source for product colour matching is a common design strategy. The process of colour scheme design involves both source colour combination optimisation and continuous variable optimisation. As a commonly used optimising tool, interactive genetic algorithm (IGA) recombines the colours between alleles, that is, the same area in different colour schemes. Two types of invalid schemes are prone to arise during optimisation. The first is the colour duplication in a colour scheme's areas, which are supposed to be painted differently. The second is the unexpected new colours generated through interpolation between alleles. This article proposes a non‐allelic IGA (NA‐IGA) method, which allows colour recombination among non‐alleles, to avoid invalid schemes and improve algorithm efficiency. The crossover, evaluation, and selection operators are redesigned. NA‐IGA tries to maintain the features of extracted colours in the colour schemes while taking user preference into consideration. Thus, interaction methods for colour matching optimisation are designed to extend the evaluation mode of traditional IGA, including colour shuffling, continuous fine‐tuning, and so forth. An application test is conducted in the rail vehicle's painting design. The comparison experiment with traditional IGA shows that NA‐IGA significantly reduces the generation of invalid schemes and significantly improves efficiency and effectiveness of the optimisation.

Evolutionary design method for transforming ceramic cultural elements into cultural and creative products

Cultural and creative products are empowered through cultural connotation in the market with ever-changing customer needs. Analyzing user preferences and matching them with product design elements has become the focus of research. Taking ceramic bracelet as the research example, this study proposed an evolutionary system which integrates the interactive genetic algorithm with artificial neural network and fuzzy analytic hierarchy process based on evaluation grid method. First, the cultural genes were extracted from cultural products as the design elements. Second, the attractiveness characteristics of the products and customer demand dimension information were analyzed by evaluation grid method. Third, the fuzzy analytic hierarchy process was used to assign weight values to the 3 users’ perceptual evaluation vocabularies. Fourth, the artificial neural network algorithm was incorporated into the fitness evaluation stage of the interactive algorithm to reduce information distortion caused by evaluation fatigue. Finally, after training the samples using the back propagation neural network, 8 design schemes with a fitness value exceeding 4.5 were obtained. The iterative curve indicated that after the 51st generation, the error accuracy approached 0.09. The experimental results verified that the system could help to improve the efficiency of product design, and design products that are more in line with user needs.

The application of interactive genetic algorithm in the optimization of medical device product form design

In order to solve the problem of low efficiency in the form design of medical device products and the inability to meet the needs of users, the research took the gastrointestinal machine of medical device as the research object, extracted the form design genes and semantic genes of medical device products, and constructed the form optimization design model of medical device products based on IGA algorithm. The research took the researcher and user data of the medical device design department as the main data, through crossover, mutation. The satisfactory solution is obtained by genetic process such as selection. Satisfactory solutions were obtained through genetic processes such as crossover, mutation and selection. The 12th generation project with the code of 000011 010001 100000 met the optimal solution, and the consensus satisfaction was 0.829. The research and design model can efficiently and automatically provide solutions for the design optimization of medical devices. It is recommended that the design department send out the best solution of the model, and optimize the solution in more detail again to improve the efficiency and accuracy of function realization.

Retracted: Innovative Design of Intangible Cultural Heritage Elements in Fashion Design Based on Interactive Evolutionary Computation

Retracted: Innovative Design of Intangible Cultural Heritage Elements in Fashion Design Based on Interactive Evolutionary Computation

Application of VR technology based on interactive genetic algorithm in the protection of ethnic intangible cultural heritage images in Guangxi

Application of VR technology based on interactive genetic algorithm in the protection of ethnic intangible cultural heritage images in Guangxi

Analysis of the application of computer-aided design in industrial product design based on

Abstract The purpose of exploring the application of computer-aided design technology in industrial product design is to realize the diversification of industrial product design and meet the personalized customization needs of customers. Starting from computer-aided technology, this paper illustrates the optimization of CAD design by using a genetic algorithm and constructs a bipolar progressive interactive genetic algorithm using bipolar progressive ranking and fuzzy fitness jointly. Genetic algorithm genotypes in CAD process product design are defined, and a comparative evaluation experimental analysis of industrial product design is performed with the AR-IGA algorithm. Regarding the number of iterations, the average number of iterations for the AR-IGA algorithm to complete the color-matching design of process products among ten testers was 9.34, which was 36.42% lower than that of the TIGA algorithm. The average evaluation elapsed time of the AR-IGA algorithm was 246.9s, which was 24.12% less than the average evaluation elapsed time of the TIGA algorithm. This shows that computer-aided technology optimized by genetic algorithms can effectively improve the efficiency of industrial product design and satisfy more customers to realize personalized design simultaneously.

Chromatics in Urban Landscapes: Integrating Interactive Genetic Algorithms for Sustainable Color Design in Marine Cities

Color design plays a pivotal role in determining whether marine urban landscapes meet the diverse needs of individuals, encompassing their visual, psychological, behavioral, and spiritual dimensions. In the contemporary urban environment, a host of practical challenges, including severe color pollution, the absence of regional cultural characteristics, and a lack of humanistic sensitivity in public spaces’ color schemes, necessitates a thorough exploration of the intricate relationship between color and human psychological needs within environmental contexts. This study leverages the potential of interactive genetic algorithms as a powerful tool for innovative color design solutions. Anchored in the nexus of the environment, human psychology, and color theory, this research delves into the intricate nuances of marine urban landscape color. Employing a comprehensive approach that combines questionnaires and extensive field surveys, we seek to elucidate the critical interplay between color and human well-being. Drawing upon the case study of Zhuhai Shijingshan Park, we harness the principles of interactive genetic algorithms to iteratively design marine urban color patterns. Through this experimentation, our study unveils novel design strategies that harmonize with the psychological idiosyncrasies of the human–environment interface. Specifically, the findings of our research highlight the profound impact of color selection on the emotional and behavioral responses of individuals within marine urban landscapes. Our study contributes vital insights into the art and science of marine urban color design, shedding light on the nuanced ways in which color choices can enhance the quality of urban spaces and promote human well-being. This research endeavors to bridge the gap between marine urban design and human psychology through a comprehensive exploration of color design in marine urban landscapes. By addressing the practical challenges and leveraging innovative design techniques, we strive to provide a holistic understanding of the symbiotic relationship between color and human experience, ultimately enriching the design practices that shape our urban environments.

Interactive Evolutionary Design of Handbag Integrating Bamboo Weaving Material

Abstract The integration and development of fashion products and intangible cultural heritage have gradually become the highlight of product design. Following thousands of years of evolution, bamboo weaving patterns offer a rich resource library for use by designers. However, the designers cannot predict which bamboo weaving pattern can be adopted into women’s handbags to satisfy consumers’ aesthetics. Therefore, taking the form design of women’s handbags as an example, this study develops an artificial intelligence-based program through which consumers can codesign with designers to effectively match the bamboo weaving pattern with the design style of women’s handbags. First, based on the coding, selection, and mutation processes of interactive genetic algorithms, consumer preferences, and perception evaluations are effectively incorporated into product design. This ensures that the evolved product solutions meet consumer preferences. Second, considering evaluator fatigue in human–computer interaction evaluation, the decision tree algorithm is used as a proxy for evaluation. Finally, the developed interactive system supports mass-generated bamboo-woven bags of different styles, allowing prediction of consumers’ preferences for the design of women’s handbags. Furthermore, the system can assist designers and bamboo craftsmen in designing other bamboo or wooden products.

Application of genetic algorithm in model music composition innovation

Abstract The genetic algorithm-assisted musical composition combines evolutionary computational techniques and artistic composition. In this article, the evolution of musical compositions based on traditional genetic algorithm compositions is combined with interactive genetic algorithm techniques, mainly in model music innovation, using a genetic algorithm to generate an initial population with real number encoding of all individuals. The article focuses on studying the fitness function used to automatically evaluate the automatically generated melodies, perform evolutionary operations on the generated melodies, and generate high-quality model music melodies based on the treatment of evolutionary operations. The evaluation time of the generated melodies by the improved model is reduced from 4 minutes to 0.002 seconds, which makes the composer much more efficient in performing model music composition. The evaluation data of 16 pieces before and after the genetic manipulation were also compared, and it was found that the system evaluated the music after the genetic manipulation significantly higher than the music without the genetic manipulation. The different forms of musical compositions created by the composing system developed based on the technical research of computer genetic algorithm-assisted model music innovation are also a useful supplement to composers’ creations and open a new chapter for the field of artificial intelligence.

Retracted: Exploration of Landscape Lighting Design Based on Interactive Genetic Algorithm.

[This retracts the article DOI: 10.1155/2022/1771617.].