Genetic Algorithm for Adaptable Design using Crowdsourced Learning as Fitness Measure

Jansson, Andreas Dyrøy; Bremdal, Bernt A.

doi:10.1109/sst.2018.8564686

Cited by 1 publication

(1 citation statement)

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“…One of the most coveted and valuable applications of ML in UX design is its ability to provide users with a new level of personalization [ 20 ]. ML algorithms that learn from usability data sources can improve the user experience [ 21 ], such as by implementing and testing a system for designing creative web elements using an interactive genetic algorithm in which voting-based feedback from the learning mechanism enables the system to adopt quality measures for visual aesthetics [ 22 ]. One systematic review of the literature that was conducted to identify the challenges UX designers face when incorporating ML into their design process contains recommendations based on its findings [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

The Application of Deep Learning for the Evaluation of User Interfaces

Keselj

Miličević

Žubrinić

et al. 2022

Sensors

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In this study, we tested the ability of a machine-learning model (ML) to evaluate different user interface designs within the defined boundaries of some given software. Our approach used ML to automatically evaluate existing and new web application designs and provide developers and designers with a benchmark for choosing the most user-friendly and effective design. The model is also useful for any other software in which the user has different options to choose from or where choice depends on user knowledge, such as quizzes in e-learning. The model can rank accessible designs and evaluate the accessibility of new designs. We used an ensemble model with a custom multi-channel convolutional neural network (CNN) and an ensemble model with a standard architecture with multiple versions of down-sampled input images and compared the results. We also describe our data preparation process. The results of our research show that ML algorithms can estimate the future performance of completely new user interfaces within the given elements of user interface design, especially for color/contrast and font/layout.

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