Interactive Genetic Algorithm Research Articles

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

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.

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

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 prom...

The Application of Big Data in Enterprise Information Intelligent Decision-Making

With the continuous increase of mass information in the process of enterprise operation, information redundancy interference poses a challenge to enterprise information decision-making. Therefore, this paper applies big data analysis technology to enterprise information intelligent decision-making, and builds an enterprise information intelligent decision-making model based on big data analysis. The key data of enterprise is mined by using the density weight Canopy to improve the K-Gmedoids algorithm. After sorting, filtering, and transforming, the big data model designed in this paper uses interactive genetic algorithms to obtain the optimal decision-making strategy of enterprise information through experimental tests, which has a significant impact on the decision-making management and the competitiveness of the enterprise. In the process of enterprise information collection and intelligent decision-making, the interactive genetic algorithm generates a 95% match between the optimal business operation plan and the problems that need to be solved in the actual operation of the enterprise, which can better improve the ability of the enterprise to deal with problems. The number of iterations of the optimal decision-making plan obtained by the company is only 6 times, which has a good use effect while improving work efficiency.

Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user’s preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks.

Decomposition-based co-evolutionary algorithm for interactive multiple objective optimization

We propose a novel co-evolutionary algorithm for interactive multiple objective optimization, named CIEMO/D. It aims at finding a region in the Pareto front that is highly relevant to the Decision Maker (DM). For this reason, CIEMO/D asks the DM, at regular intervals, to compare pairs of solutions from the current population and uses such preference information to bias the evolutionary search. Unlike the existing interactive evolutionary algorithms dealing with just a single population, CIEMO/D co-evolves a pool of subpopulations in a steady-state decomposition-based evolutionary framework. The evolution of each subpopulation is driven by the use of a different preference model. In this way, the algorithm explores various regions in the objective space, thus increasing the chances of finding DM’s most preferred solution. To improve the pace of the evolutionary search, CIEMO/D allows for the migration of solutions between different subpopulations. It also dynamically alters the subpopulations’ size based on compatibility between the incorporated preference models and the decision examples supplied by the DM. The extensive experimental evaluation reveals that CIEMO/D can successfully adjust to different DM’s decision policies. We also compare CIEMO/D with selected state-of-the-art interactive evolutionary hybrids that make use of the DM’s pairwise comparisons, demonstrating its high competitiveness.

Cognitive Association in Interactive Evolutionary Design Process for Product Styling and Application to SUV Design

In recent years, intelligent design technology that is based on interactive evolutionary algorithms, namely interactive evolutionary design (IED) systems, has received extensive attention in the computer science, design, and other related literature. However, due to the complexity of design problems and the limitation of human cognitive ability, IED faces several challenges in actual design applications. With the aim to address these problems in the IED, this paper deconstructs the IED of the product styling from the perspective of the cognitive association of the users, and proposes a corresponding cognitive intervention method that is based on the association of information. We built databases of the perceptual evaluation results of typical cases and coded profiles of the typical cases, combined with the corresponding interaction process, to improve the efficiency of creating associations between dissimilar information in the early stages of evolution. Besides, in order to simplify the process of creating associations between similar information, this paper proposes a clustering model of similar information based on explicit and implicit distances. The proposed method is then applied to the evolutionary design of an SUV. The experimental results show that the proposed method reduces the initial and total evaluation time. Therefore, the proposed method improves users’ ability to understand the complex design tasks of IED for product styling, optimizing the interactive evaluation process by guiding designers to efficiently create the cognitive association of information, and increases the effectiveness of adopting IED to solve actual design problems about product styling.

A Collaborative Effort-Benefit-Value Analysis Model to Support Requirements Reuse for Software Requirements Prioritization

This paper proposes a collaborative method to prioritize the set of requirements backing the concept of requirement re usability through crowd sourcing platform. Both stakeholder and developer perspectives are captured and effort-benefit-value analysis model is presented. Stakeholder priority is captured using Fuzzy TOPSIS model, and developer priority is captured by analyzing inter-relationships among requirements in the form of dependency. Further, a provision is provided to resolve conflicts between requirement pairs in an interactive manner to produce final ranking accepted to both with minimum conflict. An experimental proof of concept is provided by comparing results of proposed approach with state of art, fuzzy analytic hierarchy process (FAHP), interactive genetic algorithm (IGA) techniques, and on a dataset. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results in addition to handling issue of dependency, multi- criteria preferences, reliability, and robustness to errors.

Deep learning of individual aesthetics

Accurate evaluation of human aesthetic preferences represents a major challenge for creative evolutionary and generative systems research. Prior work has tended to focus on feature measures of the artefact, such as symmetry, complexity and coherence. However, research models from psychology suggest that human aesthetic experiences encapsulate factors beyond the artefact, making accurate computational models very difficult to design. The interactive genetic algorithm circumvents the problem through human-in-the-loop, subjective evaluation of aesthetics, but is limited due to user fatigue and small population sizes. In this paper, we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist’s computer art dataset, we investigate the relationship between image measures, such as complexity, and human aesthetic evaluation. We use dimension reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in a generative system. Convolutional neural networks trained on the artist’s prior aesthetic evaluations are used to suggest new possibilities similar or between known high-quality genotype-phenotype mappings. We integrate this classification and discovery system into a software tool for evolving complex generative art and design.

Who Gets Credit for AI-Generated Art?

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Analysis and Classification of Requirements Specification for Web Application Development: A Case Study Approach

In this paper, we formulated, designed, implemented and evaluated a model used for classifying stakeholders' requirements that are specified for web application development. The study employed both qualitative and quantitative research approaches in a case study. Requirements were elicited from stakeholders using the interview approach. This involved speaking with the stakeholders directly via groupware and asking them questions about their specific needs that are relevant to the development of web application. In particular, 10 customers of Procrea8 Technology Solution Limited and 9 developers were used as respondents. An interactive genetic algorithm was used to formulate the model. The design was specified using the Unified Modeling Language (UML) tool, and implemented using specified web technology tools. The model was evaluated for completeness and consistency using recall and precision as parameters. The results showed that a list of ordered requirements was produced based on the stakeholders' priorities inputted into the model. The output indicated the order of priorities finally assigned to each of the requirements. The evaluation revealed that the model is effective, efficient, userfriendly, reliable (with 96.3% accuracy), scalable (prioritized over 500 requirements), less timeconsuming (prioritizing over 500 requirements) and able to update ranks whenever changes occur automatically. Also, the model evaluation indicates 97.1% precision (consistency), and 96.0% recall (completeness). The study shows that requirements engineers could use the model to collate stakeholders’ requirements from wide geographical locations.
 Keywords: Requirements analysis, requirement prioritization, requirements engineering, web application, requirement specification.

Evolutionary Computation with User’s Preference for Solving Fuzzy Fitness Forecasting Problems

Interactive Evolutionary Computation (IEC) is a kind of human–machine interaction calculation method derived from evolutionary computation. The main problem of interactive evolutionary computation is that fitness noise can make evolution direction to deviate from user’s preferences because user’s evaluation has cognitive fluctuations and fatigue. To improve these deficiencies, this paper recommends a fuzzy fitness prediction method based on fuzzy gray model FGM (1,1) with a precise number fitness. First of all, the relationship between fitness noise intensity and the fitness function is proposed. Then, it suggests a linear programming of fuzzy fitness set width under the restriction of minimum noise intensity, which can calculate the fuzzy fitness prediction parameters. Finally, the fuzzy gray model forecasts the fuzzy fitness. The proposed method uses new computation of individual’s dominance relation and crowding distance to realize NSGA–II. The experimental results verify that this method has advantages in improving optimization quality, alleviating user’s fatigue and improving efficiency in exploration.

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

AbstractWith 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.

Restricted Boltzmann Machine-driven Interactive Estimation of Distribution Algorithm for personalized search

Effective and efficient personalized search is one of the most pursued objectives in the era of big data. The challenge of this problem lies in its complex quantifying evaluations and dynamic user preferences. A user-involved interactive evolutionary algorithm is a good choice if it has reliable preference surrogate and powerful evolutionary strategies. A Restricted Boltzmann Machine (RBM) assisted Interactive Estimation of Distribution Algorithm (IEDA) is presented to enhance the IEDA in solving the personalized search. Specifically, a dual-RBM module is developed to simultaneously provide a preference surrogate and a probability model for conducting the individual selection and generation of the IEDA. Firstly, the positive and negative preferences of the currently involved user in IEDA are distinguished and combined to achieve a dual-RBM, and then the weighted energy functions of the RBM model together with social group information from users with similar preferences are designed as the preference surrogate. The probability of the trained positive RBM on the visible units is fetched as the reproduction model of EDA since it reflects the attribute distributions of more preferred items. Some benchmarks from the Movielens and Amazon datasets are applied to experimentally demonstrate the superiority of the proposed algorithm in improving the efficiency and effectiveness of the interactive evolutionary computations served personalized search.

Category, process, and recommendation of design in an interactive evolutionary computation interior design experiment: a data-driven study

AbstractDesign is a complicated and sophisticated process with numerous existing theories trying to describe it. To verify theories and quantitatively describe the design process, design experiment, and data analysis are crucial and inevitable. However, applying data analysis in the design experiment is tricky and design data is not fully utilized in many aspects. To explore the potential of design experiment data, this paper introduces data-driven research based on an interior design experiment, aiming to reveal the category and process of design by conducting data analysis, visualization, and recommendation. We introduce an interactive evolutionary computation (IEC) design experiment that deals with a simplified interior design task and has already been tested on 230 subjects. Using the data gathered during the experiment, we conduct data analysis and visualization involving methods including Holistic color interval and K-means clustering to show categories and processes in design. Additionally, we train a content-based recommendation system with experiment data to capture user preference and make the IEC system more efficient and intelligent. The analysis and visualization show clear design categories and capture an evident trend towards the final design outcome. The application of the recommendation system brings a prominent improvement to the IEC system. This research shows the great potential of the various data-driven methods in design research.

Language model based interactive estimation of distribution algorithm

It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization problems, interactive evolutionary algorithms, which can make use of human preferences, are highly desirable. However, due to the lack of effective encoding methods, interactive evolutionary algorithms have been limited to numerically encoded optimization problems. In practice, however, linguistic terms (words) are the most natural expression of human preferences, and they are also commonly used to describe items in personalized search or E-commerce; therefore, language models better suit encoding, and the optimization of personalized search is converted into a dynamic document matching problem. To optimize word-described personalized search, we propose a novel interactive estimation of distribution algorithm. This algorithm combines a language model-based encoding approach, a Dirichlet-Multinomial compound distribution-based preference expression, and a Bayesian inference mechanism. The proposed algorithm is applied to two personalized search cases to demonstrate the capability of the algorithm in ensuring a more efficient and accurate search with less user fatigue.

Creation of Facial Composites from User Selections using Image Gradient

Evolutionary facial composites are created using interactive genetic algorithms based on user selections. This approach is grounded in perceptive studies, and is superior to feature-based systems. A method is presented for creating facial composites in which faces are encoded with shape information, the coordinates of a predefined landmark points, and the image gradient, which represents face information more precisely than image luminance. The new method is accompanied by a Poisson integration process that presents the user with candidate faces. Two user tests, one using composite creators and the other external evaluators, show that the new method produces higher rated composites that are better recognised

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

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.

The Fractal Artistic Design Based on Interactive Genetic Algorithm

The Fractal Artistic Design Based on Interactive Genetic Algorithm

Generating Kranok patterns with an interactive evolutionary algorithm

The kranok pattern is a classical Thai pattern, often seen in the ornamentation of Thai religious artifacts such as Tripiṭaka cabinets, temple doors and coffins. All Thai trainee artists must practice drawing the kranok pattern because it is a fundamental motif in Thai traditional decorative art. Individual skilled artists and instructors have their own preferred ways of drawing the pattern, and their styles differ in ornamental details. Nevertheless, all make use of similar basic structures. Most trainees learn a range of styles by observing experienced artists. After exploring the methods of drawing used by experts and published in textbooks, we designed an algorithm for automatically generating kranok patterns. We used an interactive evolutionary algorithm (IEA) to improve the aesthetic appeal of the generated patterns in response to users’ feedback. The aim of our work was not to replace artists with machines, but to enhance human artistic expression. Specifically, the work aimed to help users without artistic skills to create kranok patterns in their own style. The algorithm facilitated the creation of a variety of personalized kranok patterns – diverse in their expressive curvature, refinement and proportions – that were satisfying to the varying preferences of a range of users. We also analyzed the proposed algorithm’s behavior in terms of its convergence to generate specific shapes. The proposed method was examined by 28 respondents (27 Thai and 1 foreign) who were selected to include representatives of both sexes as well as experts in both Thai drawing and evolutionary algorithms. The results from our questionnaires showed that all respondents were satisfied with the generated kranok patterns: one respondent was ‘completely satisfied’, seventeen ‘very satisfied’, seven ‘moderately satisfied’, and three ‘slightly satisfied’.

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

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.