Interactive Genetic Algorithms for User Interface Design

Abstract-Genetic algorithms have been used extensively in solving complex solution-space search problems. However, certain problems can include multiple sub-problems in which multiple searches through distinct solution-spaces are required before the final solution combining all the sub-solutions is found. This paper presents a generic design of genetic algorithms which can be used for solving complex solution-space search problems that involve multiple sub-solutions. Such problems are very difficult to solve using basic genetic algorithm designs that utilize a single gene-set per chromosome. The suggested algorithm presents a generic solution which utilizes both multi-gene-set chromosomes, and an adaptive gene mutation rate scheme. The results presented from experiments done using an automatic graphical user interface generation case study, show that the suggested algorithm is capable of producing successful solutions where the common single-gene-set design fails.

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“…The problem of automated graphical user interface generation using GAs was discussed in [24][25][26].…”

Section: Implementation and Resultsmentioning

confidence: 99%

A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA)

Maaita¹,

Zraqou²,

Hamad³

et al. 2015

ijacsa

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Section: Interactive Evolutionary Computingmentioning

confidence: 99%

“…However, it is difficult to make user models,because the preferences of users are relative to the emotion, the intuition, and the domain knowledge about what they are evaluated [4].With the help of IEC, a user's psychological space is mapped to a EA's parameter space. During the past decades, swarm intelligence algorithms including GA(Genetic Algorithm),PSO(Particle Swarm Optimization) and DE(Differential Evolution)were introduced into IEC [4,7,8]. In our proposed method, we implemented the interactive evolution computation framework based on GA. A user assigns fitness to an individual in the following three ways:1)assigning a number on a subjective grading scale;2)ranking the individual;3)choosing the best individual from a displayed subset.The first way to get fitness was chosen in our proposed IGA.In this way, the best and the worst solution will be easy to get in every iteration.However, because of the continuous work, the user usually falls into a state of fatigue after evaluation of 20-30 generations [2].…”

Section: Interactive Evolutionary Computingmentioning

confidence: 99%

“…Among these researches and applications, the Interactive Genetic Algorithm(IGA) is commonly used. During the last decade,it has been widely applied to deal with several areas of application, like Facility Layout Design(FLD) [3] ,user interface design [4],fashion design [5], and so on. But little literature considers the cover design which employs the user subjective evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Developing support system for cover design with interactive evolutionary computation

Kuang

et al. 2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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we developed an automatic cover design supporting system for non-professional users to create their preferred cover designs in a user-oriented way by using Interactive Genetic Algorithm(IGA).Different from the template design pattern, our system allows an interaction between the algorithm and the Decision Maker(DM).It allows the users to take into account their preferences at each generation of IGA.In order to avoid the fatigue of the users, a novel evaluation model consisting of DM evaluation and machine evaluation is applied.The whole population is classified into clusters by the K-means clustering algorithm and only the most representative individual of each cluster is evaluated by the DM.The support system is applied to a book cover design.The results show that the proposed IGA is able to capture preferences of user in a reasonable number of iterations.

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“…It is shown that these operations reduce the number of generations. Another method is proposed by Quiroz et al for user interface design [23], in which the user just selects the best and worst designs in each generation. Then, the other designs are scored based on their similarity to these chromosomes.…”

Section: Review Of Literature On Reducing User Fatigue In Interactivementioning

confidence: 99%

Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs

Darani

Kaedi

2017

Hum. Cent. Comput. Inf. Sci.

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IntroductionAppearance of products is one of the factors of customer attraction. This necessitates customer-centric design of products and product customization [1][2][3]. One of the methods proposed in this area is the direct use of designs proposed by users for designing the products. However, offering a design requires specialized knowledge and necessary skills for working with design software and tools and customers lack such requirements. Besides, strong presence in competitive markets requires quick development of AbstractOne of the effective factors in increasing sales is the consistency of products with the preference of the customers. Designing the products consistent with customer needs requires the engagement of customers in the product design process. One way to achieve this goal is the use of interactive evolutionary algorithms. During the running of such algorithms, the customer acts as a fitness function and imparts his/her opinion directly to the design process. Since these algorithms are usually iterated frequently, the user fatigue problem during interaction with them is a major challenge. The present study develops a method to tackle the user fatigue problem in the interactive genetic algorithm using the candidate elimination algorithm. In this method, customer preferences are gradually learned by applying the candidate elimination algorithm on the designs evaluated by the user in the early stages of algorithm. Using the learned preferences, designs which may not meet the customer preferences are discovered and automatically receive a predefined low score from the algorithm. The proposed method has been evaluated on the customer-centric design of book covers and its results have been compared with those of the two simple interactive genetic algorithm and multi-stage interactive genetic algorithm. The results are indicative of a considerable reduction of the number of algorithm generations, the number of chromosomes being evaluated by user, and the evaluating time in comparison with the two aforementioned methods. Reduction of these criteria leads to decrease of user fatigue. In addition, the proposed method has increased the user satisfaction. Keywords:Interactive design, Customer-centric product design, Interactive genetic algorithm, User fatigue, Candidate elimination algorithm Open Access© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sheikhi Darani and Kaedi Hum. Cent. Comput. Inf. Sci. (2017) Page 2 of 18 Sheikhi Darani and Kaedi Hum. Cent. Comput. Inf. Sci. (2017) 7:38 customer-centric designs for products and services. These factors have paved the way for the emergence of interactive evolutionary algorithms in the design ...

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Interactive Genetic Algorithms for User Interface Design

Cited by 38 publications

References 7 publications

A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA)

A Generic Adaptive Multi-Gene-Set Genetic Algorithm (AMGA)

Developing support system for cover design with interactive evolutionary computation

Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs

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