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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  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
  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.",

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