Using Interactive Genetic Algorithm
Using Interactive Genetic Algorithm
Web sites are very popular at present. Many new web sites occur daily on the Internet [1]; thus, the requirements to create and design web pages are increased. These require users to know programming languages and tools for developing and designing web pages. It takes time to develop and design web pages as their need. In order to reduce time from both creating and designing web pages, they have to find a new solution by using web page templates. Web page templates consist of HTML files and CSS files. HTML files hold the structure and content of the page and CSS Style Sheet files hold the presentation styles of pages. Therefore, we propose a new web application which is developed by applying interactive genetic algorithm which requires the involvement of users to interact with the web application. The interactive genetic algorithm has been applied to many applications in design such as a fashion design which is used to model women’s dress [2], a Japanese Kimono design to model Yukata which is a traditional Japanese garment often worn in mid-summer [3], a font generation system which is designed to emerge various fonts based on user’s Kansei without hand drawing [4], evolvingcolors in user interfaces to search for a solution that provides a good trade-off between aesthetics and accessibility requirements [5], an office layout support system which can generate not only in square space but also in polygonal space [6], a user interface design which evolve user interfaces in the XUL interface definition language which is a user interface markup language developed by the Mozilla project [7], a sign sound design which is to generate melody based sounds freely and easily [8], a web site design system which has users to be involved in the process to generate web page [9, 12]. The difference between this work and the previous work [9, 12] is how to rate a web page template. In the previous work, users are involved in the evaluation process and they can rate a preferable web page template one at a time. The problem is we cannot know which section of the web page template they like or dislike. Thus, we have divided the web page template into many sections and users can rate each section of the web page template for what they like or dislike. By applying interactive genetic algorithm, the proposed algorithm generates many web page templates with different layouts for users to choose. Users just rate each section of the web page templates, such as header, footer, sidebar, etc., to express their preference how much they like or dislike it. Then, the algorithm will evolve a new population of web page templates based on the old ones according to users’ preferences Users can view and download web page templates which are created by the web application to apply these templates to their creation and design tasks. The details will describe in the remaining of this paper which is organized as follows: section II describes the background of genetic algorithm and interactive genetic algorithm, section III presents the proposed method, section IV describes the experiments and results, and section V is a conclusion
creates the initial population of solutions and uses genetic operators such as selection, crossover, and mutation to create offspring. The solutions are gradually improved by a selection scheme which selects the survivors by their fitness values. 2)Interactive Genetic Algorithm Interactive genetic algorithm and genetic algorithm are similar, except an evaluation process. The interactive genetic algorithm requires users to give fitness to each individual instead of using a fitness function. The fitness here is a measure of how well each potential solution solves the problem at hand. The interactive genetic algorithm is applied to many application domains where the fitness function is difficult or impossible to design a computational fitness function https://arudhrainnovations.com/
the process of using the interactive genetic algorithm for generating web page templates. The web application creates an initial population of 10 web page templates which are the base population for creating the next population of web page templates. Then, it displays 10 web page templates to users. If the web page templates satisfy users, the process of the web application is finished; otherwise, users have to rate each section of every web page template for generating the next population. And then, the web application calculates a total score of every web page template and selects 2 web page templates which are not the same from the base population. A higher score template has more probability to be selected than a lower score template. After the 2 web page templates are selected, the web application generates a random value to decide which genetic operator is selected for producing an offspring. In this paper, we define a crossover rate as 0.7. If the random value is smaller than 0.7, a crossoveroperator is selected; otherwise, a mutation operator is selected. The web application continues producing many offspring until it reaches the size of the population which has 10 web page templates and displays the new offspring to users. The process continues doing many times and users are required to rate until requirements of users are met.Genetic Encoding In this paper, each web page template is encoded as a chromosome which is divided into 2 parts, a layout part and a style part. The layout part consists of layout, container, header, navigation top, sidebar left, content, sidebar right, navigation bottom, and footer gene; the style part consists of body, header1, header2, header3, paragraph, list, image, anchor, and color scheme gene.below shows the structure of the chromosome. All gene encodings are described in Table I and web page layouts.web desinging company kumbakonam
https://goo.gl/maps/3XfS22poCJCfCTqH8
Web sites are very popular at present. Many new web sites occur daily on the Internet [1]; thus, the requirements to create and design web pages are increased. These require users to know programming languages and tools for developing and designing web pages. It takes time to develop and design web pages as their need. In order to reduce time from both creating and designing web pages, they have to find a new solution by using web page templates. Web page templates consist of HTML files and CSS files. HTML files hold the structure and content of the page and CSS Style Sheet files hold the presentation styles of pages. Therefore, we propose a new web application which is developed by applying interactive genetic algorithm which requires the involvement of users to interact with the web application. The interactive genetic algorithm has been applied to many applications in design such as a fashion design which is used to model women’s dress [2], a Japanese Kimono design to model Yukata which is a traditional Japanese garment often worn in mid-summer [3], a font generation system which is designed to emerge various fonts based on user’s Kansei without hand drawing [4], evolvingcolors in user interfaces to search for a solution that provides a good trade-off between aesthetics and accessibility requirements [5], an office layout support system which can generate not only in square space but also in polygonal space [6], a user interface design which evolve user interfaces in the XUL interface definition language which is a user interface markup language developed by the Mozilla project [7], a sign sound design which is to generate melody based sounds freely and easily [8], a web site design system which has users to be involved in the process to generate web page [9, 12]. The difference between this work and the previous work [9, 12] is how to rate a web page template. In the previous work, users are involved in the evaluation process and they can rate a preferable web page template one at a time. The problem is we cannot know which section of the web page template they like or dislike. Thus, we have divided the web page template into many sections and users can rate each section of the web page template for what they like or dislike. By applying interactive genetic algorithm, the proposed algorithm generates many web page templates with different layouts for users to choose. Users just rate each section of the web page templates, such as header, footer, sidebar, etc., to express their preference how much they like or dislike it. Then, the algorithm will evolve a new population of web page templates based on the old ones according to users’ preferences Users can view and download web page templates which are created by the web application to apply these templates to their creation and design tasks. The details will describe in the remaining of this paper which is organized as follows: section II describes the background of genetic algorithm and interactive genetic algorithm, section III presents the proposed method, section IV describes the experiments and results, and section V is a conclusion
creates the initial population of solutions and uses genetic operators such as selection, crossover, and mutation to create offspring. The solutions are gradually improved by a selection scheme which selects the survivors by their fitness values. 2)Interactive Genetic Algorithm Interactive genetic algorithm and genetic algorithm are similar, except an evaluation process. The interactive genetic algorithm requires users to give fitness to each individual instead of using a fitness function. The fitness here is a measure of how well each potential solution solves the problem at hand. The interactive genetic algorithm is applied to many application domains where the fitness function is difficult or impossible to design a computational fitness function https://arudhrainnovations.com/
the process of using the interactive genetic algorithm for generating web page templates. The web application creates an initial population of 10 web page templates which are the base population for creating the next population of web page templates. Then, it displays 10 web page templates to users. If the web page templates satisfy users, the process of the web application is finished; otherwise, users have to rate each section of every web page template for generating the next population. And then, the web application calculates a total score of every web page template and selects 2 web page templates which are not the same from the base population. A higher score template has more probability to be selected than a lower score template. After the 2 web page templates are selected, the web application generates a random value to decide which genetic operator is selected for producing an offspring. In this paper, we define a crossover rate as 0.7. If the random value is smaller than 0.7, a crossoveroperator is selected; otherwise, a mutation operator is selected. The web application continues producing many offspring until it reaches the size of the population which has 10 web page templates and displays the new offspring to users. The process continues doing many times and users are required to rate until requirements of users are met.Genetic Encoding In this paper, each web page template is encoded as a chromosome which is divided into 2 parts, a layout part and a style part. The layout part consists of layout, container, header, navigation top, sidebar left, content, sidebar right, navigation bottom, and footer gene; the style part consists of body, header1, header2, header3, paragraph, list, image, anchor, and color scheme gene.below shows the structure of the chromosome. All gene encodings are described in Table I and web page layouts.web desinging company kumbakonam
https://goo.gl/maps/3XfS22poCJCfCTqH8
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