Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Accéder directement au contenu
Article Dans Une Revue Evolutionary Computation Année : 2017

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search

Résumé

We evaluate and analyse a framework for Evolutionary Visual Exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional datasets towards two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this paper, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clear predefined task, that examines how users leverage the system and how the system evolves to match their needs. While previously we showed that using EVE, domain experts were able to formulate interesting hypothesis and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating Interactive Evolutionary Computation (IEC) techniques based on user study methodologies.
Fichier principal
Vignette du fichier
boukhelifa_eve_preprint.pdf (6.21 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01218959 , version 1 (21-10-2015)

Identifiants

Citer

Nadia Boukhelifa, Anastasia Bezerianos, Waldo Cancino, Evelyne Lutton. Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search. Evolutionary Computation, 2017, 25 (1), pp.55-86. ⟨10.1162/EVCO_a_00161⟩. ⟨hal-01218959⟩
504 Consultations
492 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More