Interactive Genetic Algorithm Research Articles

Retracted] Designing Decorative Flower Patterns for Clothing Using Genetic Algorithm

This paper aims to incorporate artistic vocabulary from intangible cultural heritage into historical clothing designs, particularly beautiful pattern designs. It is based on a profound understanding of the concept of immaterial cultural heritage and the mastery of its traditional skills to meet the public’s needs in terms of clothing and flowers. Technology, culture, and intangible cultural legacies can all be passed down and incorporated into the design. It is using interactive evolutionary computing to investigate the intangible cultural heritage elements of designing novel decorative flower patterns for clothing. Due to the rapid advancement of digital technology, computers are now used more frequently in the design of patterns and clothing. The goal is to improve the production of artistic trends and decrease the use of manual and traditional methods. This study uses interactive genetic algorithms to examine the current state of intangible cultural heritage elements in building designs for decorative flower patterns for clothing. The application of genetic algorithms to the design of clothing with flowers as decorative patterns is the main focus of this paper. The genetic algorithm is described, followed by an analysis of its fundamental structure. Then, the characteristics of the clothing flower decorative pattern are determined, the genetic algorithm model is applied to the design of the decorative flower patterns for clothing, and the hierarchical structure of heredity, variation, and interaction is provided. Finally, the genetic algorithm is applied to the large‐scale generation and mutation optimization of designing decorative flower patterns for clothing after analyzing its application in clothing flower decorative pattern technology. The idea presented in this paper can self‐reproduce and optimize, resulting in the nonartificial generation and optimization of designing decorative flower patterns for clothing.

Optimization of the Virtual Scene Layout Based on the Optimal 3D Viewpoint

At present, visual and parametric designs have been widely used in architectural design, urban planning and landscape design for design inspiration. Highly integrated auxiliary design software has become more prevalent and greatly changed the traditional way of working with architectural design and urban planning with parameterised operations, convenient editing methods and efficient workflows. However, these methods for layout optimisation have more or fewer problems, such as humdrum forms, lack of aesthetic principles, and difficulty adapting to the layout optimisation of complex architectural environments, and usually presented obstacles to the users’ understanding of hierarchical relationships in complex environments. This paper presents a novel 3D scene layout optimisation method that combines scene view information with an interactive genetic algorithm to optimise the virtual environment’s layout. We apply the visual perception information of 3D building models to improve the scene’s layout and integrate the evaluation of visual salient features with other factors to realise the intelligent scene’s layout based on optimal 3D viewpoints and a genetic algorithm. The experimental results show that our method can efficiently optimise the layout of the 3D building environment, and it has innovation and practical application value in the fields of architectural visualisation, 3D animation, landscape planning and game design.

Applications of affordance and cognitive ergonomics in virtual design: A digital camera as an illustrative case

Affordance-based design (ABD) plays an important role in identifying interactions, especially effortless ones, between users and artifacts. Cognitive ergonomics extends our understanding of this effortless interaction. This study combines the two design methodologies together in order to reduce cognitive friction in using digital products. The design process of a compact digital camera is selected as a case study that includes the design of the physical shape for a camera and of its user interface. In designing a product shape, a design toolbox was developed that integrated a modified multi-objective genetic algorithm and the ABD, which was named as affordance-based interactive genetic algorithm. Using this toolbox and interactive user feedback, the camera design evolves toward a product that better satisfies the users. User interfaces (UIs) including linear and elliptic layouts were subsequently designed based on cognitive ergonomics. A predictive tool of UI, the Cog Tool, was used to evaluate the performance of skilled users on a given task by correlating the overall task completion time. Finally, this research has the potential to not only effectively address the shortcomings of the design of consumer electronics but also enrich the generation of design solutions during the preliminary design phase of such products.

Requirements traceability recovery for the purpose of software reuse: an interactive genetic algorithm approach

Requirements traceability recovery for the purpose of software reuse: an interactive genetic algorithm approach

Computer-assisted Music Composition Algorithm Design Dependent on Interactive Genetic Algorithm with Interval Fitness

Computer-assisted music composition refers to computer-assisted music composition with the participation of people. However, there are problems such as style and expression. In this paper, a computer-assisted music composition algorithm based on the interactive genetic algorithm with interval fitness is proposed. A new music prediction model is established by integrating melody units and rhythms into traditional models with only notes or rhythms as units. Moreover, the generated music phrases are optimized by the interactive genetic algorithmphrase. The simulation results suggest that the proposed algorithm can generate music phrases quickly with a certain melody logic that conforms to the personal demand of users using a small data set.

Using evolutionary algorithms to determine the environmental projects effectiveness

Environmental projects have a high degree of uncertainty and risk of their implementation. In the process of assessing their effectiveness, it is necessary to take into account the interests of all the participants in the environmental project. The environmental projects effectiveness evaluation is formalized in the form of a mathematical problem of nonlinear programming with Boolean variables with constraints such as equality and inequality. To solve it, the interactive genetic algorithm for the environmental projects implementation trajectory formation was developed.

Research on interactive color design method of cultural creative product driven by group consensus

Aiming at assisting the color design of cultural creative product and outputting color schemes that correspond to multi-user image preference effectively, an interactive color design method driven by group consensus was proposed in this paper. Firstly, the triangular fuzzy number was used to quantitatively descript users' image preference and a consensus degree model of group users' image preference was constructed. By studying the process and basic principles of interactive genetic algorithms, the interactive evolution operation for cultural creative product color design was then implemented based on the group consistency of decision-making combined with the triangular fuzzy number, which can help generate cultural creative product color schemes that satisfy group consensus and satisfaction. Next, the color aesthetic measure model was used to rank and select the final product color design schemes. Finally, taking the color design of terracotta warriors' statue as an example, it is verified that the proposed method can effectively integrate the image preference of group users, and assist designers to better conduct cultural creative product color design with the consistent results of multi-user decisions.

Product Packaging Design Innovation based on Visual Communication in Digital Perspective

Objectives: This paper chose the front section of the assembly line of the explosive packaging machine as the design object, and studied the application of the interactive genetic model in the packaging machinery. Methods: Through the research on the product characteristics and the development trend of the packaging machinery, the main points of the design of the packaging machinery were summarized. The application models of two interactive genetic algorithms based on the compound fitness allocation strategy (HFAS) and the graded fitness allocation strategy were established in this paper. Results: The parameter setting of the compound fitness distribution strategy was analyzed, and the better scheme was selected for the case of the packaging machine design. Conclusion: In this paper, a specific case of the application of interactive genetic algorithm in the design of packaging machinery products—the design of the front section of the explosive packing machine was discussed. The total arrangement method and fuzzy evaluation method were used to evaluate the design results. Two methods were used to evaluate the design results.

A smart knowledge deployment method for the conceptual design of low-carbon products

As the consciousness of the global environment and sustainability has increased, low-carbon products have played a vital role in the transformation to a circular economy. Advanced smart design technology has enabled product designers to fulfill customer requirements by offering tailor-made functions and low-carbon solutions. However, although the existing approaches used in the conceptual design process can help in functional reasoning, knowledge modelling, and scheme evaluation, the smart reuse of knowledge, such as in design model improvement and concept scheme iteration for lower carbon emission, the corresponding process evaluation concerning carbon footprint has not been given sufficient attention. To resolve this, in this work, a smart knowledge deployment method is proposed for reasoning, configuring, and optimizing the conceptual scheme (CS) based on carbon emission evaluation and interaction. First, to match discretized knowledge, sub-function requirements after function decomposition are mapped with granular clustered knowledge into a matrix based on a requirement function knowledge deployment (RFKD) model. Second, the derived candidate concept schemes (CCSs) are selected in three steps: conflict-based primaries, configuration, and carbon footprint ranking. Finally, the initial conceptual scheme (ICS) with the lowest carbon emission is used as input for the interactive genetic algorithm (IGA) to better capture a comprehensive set of user feedback on potential candidate schemes through interactions. Accordingly, improvements are completed as intended. The prototype design and an experimental study of a brand-new friction-wear testing machine are conducted. The results suggest that the proposed approach could effectively reduce the carbon emissions of products obtained through CS and improve the convergence of the schemes produced via genetic operation.

Propeller optimization by interactive genetic algorithms and machine learning

ABSTRACT Marine propeller design can be carried out with the aid of automated optimization, but experience shows that a such an approach has still been inferior to manual design in industrial scenarios. In this study, the automated propeller design optimization is evolved by integrating human–computer interaction as an intermediate step. An interactive optimization methodology, based on interactive genetic algorithms (IGAs), has been developed, where the blade designers systematically guide a genetic algorithm towards the objectives. The designers visualize and assess the shape of the blade cavitation and this evaluation is integrated in the optimization method. The IGA is further integrated with a support-vector machine model, in order to avoid user fatigue, IGA's main disadvantage. The results of the present study show that the IGA optimization searches solutions in a more targeted manner and eventually finds more non-dominated feasible designs that also show a good cavitation behaviour in agreement with designer preference.

무리지능 기반 파라메트릭 파사드 패턴 디자인 프로세스 및 그래스호퍼 알고리즘 개발

As a follow-up research, this research aims to develop a Swarm Intelligence-based parametric facade symmetric pattern design process using Interactive Genetic Algorithm from Genetic Algorithm in order to drawing out Swarm Intelligence-Based Parametric Facade Pattern Design Methods, which are so called ‘Population Position’, Three Behaviors’, and ‘Flow’, developed by Oh, Y. E. and Lee, H. S. (2021). This study’s method is first, to theoretically review Genetic Algorithm as a design process platform, often utilized in Biomimicry, second, to find out basic elements making up a parametric facade pattern design, to be compared with Interactive-Genetic Algorithm process and parametric facade pattern design process for building as a design process platform, and to develop Swarm Intelligence-based parametric facade symmetric pattern design process using Interactive-Genetic Algorithm, third, to step on the developed design process, while showing each step’s element with generated results by the design methods, fourth, to compare and analyze the developed design process with previous researches in several aspects. It means to have built a systematic form as a parametric facade pattern design process for generating Swarm Intelligence-based parametric facade pattern designs. This paper will be expected to help designers and architects get inspired by parametric facade pattern designs generated through the developed design process and come up with creative and various idea in their design process.

EXTRACTING DESIGN RECOMMENDATIONS FROM INTERACTIVE GENETIC ALGORITHM EXPERIMENTS: APPLICATION TO THE DESIGN OF SOUNDS FOR ELECTRIC VEHICLES

AbstractThe integration of users' perception in the design process is and important challenge for the optimization of products. This study describes how design recommendations can be drawn, from a perceptual experiment with a panel of subjects using a multi-objective interactive genetic algorithm (IGA). The application concerns the bi-objective optimization of the unpleasantness and the detectability of sounds for electric vehicles (EV). After a description of the experimental protocol for the assessment of the detectability and the unpleasantness of EV sounds (listening test), a set of optimal sounds (Pareto efficient) is defined with an IGA experiment. The analysis of these sounds, based on a probabilistic analysis of the selection process, leads to the definition of design recommendations. A second listening test, involving recommended sounds but also other design proposals, allows an evaluation of the validity of the approach. Results show that the sounds recommended obtain interesting performance, in particular to improve the detectability of EV sounds.

A two-stage information retrieval system based on interactive multimodal genetic algorithm for query weight optimization

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.

Demo: A Computer-Assisted Approach to Composing with MaestroGenesis

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces.

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

The application of intangible cultural heritage cultural elements and traditional crafts in modern design, especially in modern fashion design, is not to flatter the public but to integrate the artistic language of intangible cultural heritage into modern fashion on the basis of deeply understanding the connotation of intangible cultural heritage and mastering its traditional crafts, so as to meet people’s demand for fashion and aesthetics. It can also promote the inheritance and development of traditional intangible cultural heritage culture and technology. The purpose of this study is to analyze the intangible cultural heritage elements in the innovative design of fashion design by using interactive evolutionary computation. According to the composition characteristics of cultural elements, this research uses interactive evolutionary calculation to analyze the current status of intangible cultural heritage elements in clothing design. Then, 30 fashion designers are selected to evaluate the design situation and judge the effect of the method on the design. The results show that the neural network has evaluated 36 generations, that is, 256 times of moderate value. Compared with the general IGA algorithm, adding neural network IGA can reduce the fatigue caused by user reference score and improve the quality of the optimal solution, and the cultural image attributes whose perception frequency is more than 50% are favored. It is concluded that the research method in the intangible cultural heritage elements in fashion design can improve user satisfaction and the effect is good. This research contributes to the application of intelligent algorithm in the field of fashion design.

A learning interactive genetic algorithm based on edge selection encoding for assembly job shop scheduling problem

This paper presents an effective learning interactive genetic algorithm based on edge selection encoding (LIGA-ESE) for solving the assembly job shop scheduling problem (AJSSP), with the aim of minimizing the maximum completion time (makespan). In the LIGA-ESE, a novel ESE based on virtual components for producing every feasible solution generated with acceptable probability is proposed to improve the convergence efficiency. Based on the proposed competition and learning interactive mechanisms, two genetic algorithm populations from obverse and reverse schedules, respectively, compete to improve the utilization of computation resources and the quality of populations. In addition, the influences of parameters are considered, and a learning parameter combination selection mechanism based on historical optimization performance is proposed for further improving the convergence efficiency of the LIGA-ESE. Finally, a numerical simulation is conducted based on widely used AJSSP instances. Comparisons between the LIGA-ESE and existing algorithms and other LIGA-ESE versions demonstrate its effectiveness and superiority in solving the AJSSP.

Exploration and Visualization of Patterns Underlying Multistakeholder Preferences in Watershed Conservation Decisions Generated by an Interactive Genetic Algorithm

AbstractIn multiple watershed planning and design problems, such as conservation planning, quantitative estimates of costs, and environmental benefits of proposed conservation decisions may not be the only criteria that influence stakeholders' preferences for those decisions. Their preferences may also be influenced by the conservation decision itself—specifically, the type of practice, where it is being proposed, existing biases, and previous experiences with the practice. While human‐in‐the‐loop type search techniques, such as Interactive Genetic Algorithms (IGA), provide opportunities for stakeholders to incorporate their preferences in the design of alternatives, examination of user‐preferred conservation design alternatives for patterns in Decision Space can provide insights into which local decisions have higher or lower agreement among stakeholders. In this paper, we explore and compare spatial patterns in conservation decisions (specifically involving cover crops and filter strips) within design alternatives generated by IGA and noninteractive GA. Methods for comparing patterns include nonvisual as well as visualization approaches, including a novel visual analytics technique. Results for the study site show that user‐preferred designs generated by all participants had strong bias for cover crops in a majority (50%–83%) of the subbasins. Further, exploration with heat maps visualization indicate that IGA‐based search yielded very different spatial patterns of user‐preferred decisions in subbasins in comparison to decisions within design alternatives that were generated without the human‐in‐the‐loop. Finally, the proposed coincident‐nodes, multiedge graph visualization was helpful in visualizing disagreement among participants in local subbasin scale decisions, and for visualizing spatial patterns in local subbasin scale costs and benefits.

Research on intelligent design mechanism of landscape lamp with regional cultural value based on interactive genetic algorithm

SummaryThis article aims to study the design mechanism of landscape lamp modeling based on regional cultural characteristics by using intelligent design methods. Big data technology is used to mine regional cultural level factors from four dimensions: regional, cultural, artistic, and functional. The weight values of all these factors are ranked by analytic hierarchy process (AHP) evaluation system. Meanwhile, the objective fitness of interactive genetic algorithm (IGA) is constructed according to the weight values. Design rules are added to constrain individuals to influence the iteration and output of design process by intelligent means. The intelligent design mechanism of landscape lamp with regional cultural value is constructed by IGA. The intelligent design mechanism based on AHP‐IGA is helpful to improve the iterative efficiency of interactive process and reduce project development cost.

An Interactive Multi-Agent System for Game Design

This paper presents a novel approach to procedural generation of game maps for multi-player, competitive video games. A multi-agent evolutionary system is employed to place streets, buildings and other items, resulting in a playable video game map. The system utilises computational agents that act in conjunction with the human designer to produce maps that exhibit desirable characteristics. This paper compares the impact that the additional agents have in terms of the quality of candidate solutions. The results indicate that the use of the agents produces higher quality solutions in comparison to a traditional interactive genetic algorithm.

Rural road ecological landscape planning system based on interactive genetic algorithm

Traditional vegetation landscape spatial hierarchical planning system is faced with huge and complex real data, because its throughput is too low to get more accurate data, this paper proposes a rural road ecological landscape planning system based on interactive genetic algorithm. In the hardware design of the system, the structure distribution of ZingRDK feature extraction hardware is mainly designed, and the functions and parameters of the chip and HDMI interface in the structure are designed in detail. In the software design of the system, the interactive genetic algorithm is applied to the planning process, and then the visual feature reconstruction method is used to collect the green plant landscape image. The edge contour feature of the collected image is extracted, and the color component fusion method is used to enhance the information of green plant landscape image. The intuitionistic ambiguity set of multi-resolution landscape image is given, and the intelligent landscape planning result is calculated by fuzzy information clustering method. The results of the system test show that the net data throughput of the designed rural road landscape planning system is greater than that of the traditional planning system, which indicates that the plant landscape planning system has stronger ability to acquire and process data.