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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Boukhelifa:2016:EC,

• author = "Nadia Boukhelifa and Anastasia Bezerianos and Waldo Cancino and Evelyne Lutton",
• title = "Evolutionary Visual Exploration: Evaluation of an {IEC} Framework for Guided Visual Search",
• journal = "Evolutionary Computation",
• year = "2017",
• volume = "25",
• number = "1",
• pages = "55--86",
• month = mar,
• keywords = "genetic algorithms, genetic programming, interactive evolutionary computation, visual analytics, information visualization, data mining, interactive evolutionary algorithms",
• publisher = "HAL CCSD; Massachusetts Institute of Technology Press (MIT Press)",
• ISSN = "1063-6560",
• annote = "Universit{\'e} Paris-Sud - Paris 11 (UP11); Interacting with Large Data (ILDA) ; Laboratoire de Recherche en Informatique (LRI) ; Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - Universit{\'e} Paris-Sud - Paris 11 (UP11) - Centre National de la Recherche Scientifique (CNRS) - INRIA Saclay - Ile de France ; INRIA - INRIA; G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires (GMPA) ; AgroParisTech (AgroParisTech) - Institut national de la recherche agronomique (INRA)",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Analysis and Visualization (AVIZ) and INRIA Saclay - Ile de France and INRIA - INRIA and Interacting with Large Data and G{\'e}nie et Microbiologie des Proc{\'e}d{\'e}s Alimentaires",
• identifier = "hal-01218959",
• language = "en",
• oai = "oai:HAL:hal-01218959v1",
• relation = "info:eu-repo/semantics/altIdentifier/doi/10.1162/EVCO_a_00161",
• URL = "https://hal.inria.fr/hal-01218959",
• URL = "https://hal.inria.fr/hal-01218959/document",
• URL = "https://hal.inria.fr/hal-01218959/file/boukhelifa_eve_preprint.pdf",
• DOI = "DOI:10.1162/EVCO_a_00161",
• size = "32 pages",
• abstract = "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.",
• notes = "Also known as \cite{boukhelifa-EVCO2016}",

}

Genetic Programming entries for Nadia Boukhelifa Anastasia Bezerianos Waldo Cancino Evelyne Lutton

Citations