Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Boukhelifa:2016:EC,
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author = "Nadia Boukhelifa and Anastasia Bezerianos and
Waldo Cancino and Evelyne Lutton",
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title = "Evolutionary Visual Exploration: Evaluation of an
{IEC} Framework for Guided Visual Search",
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journal = "Evolutionary Computation",
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year = "2017",
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volume = "25",
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number = "1",
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pages = "55--86",
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month = mar,
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keywords = "genetic algorithms, genetic programming, interactive
evolutionary computation, visual analytics, information
visualization, data mining, interactive evolutionary
algorithms",
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publisher = "HAL CCSD; Massachusetts Institute of Technology Press
(MIT Press)",
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ISSN = "1063-6560",
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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",
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identifier = "hal-01218959",
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language = "en",
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oai = "oai:HAL:hal-01218959v1",
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relation = "info:eu-repo/semantics/altIdentifier/doi/10.1162/EVCO_a_00161",
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URL = "https://hal.inria.fr/hal-01218959",
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URL = "https://hal.inria.fr/hal-01218959/document",
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URL = "https://hal.inria.fr/hal-01218959/file/boukhelifa_eve_preprint.pdf",
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DOI = "DOI:10.1162/EVCO_a_00161",
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size = "32 pages",
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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.",
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notes = "Also known as \cite{boukhelifa-EVCO2016}",
- }
Genetic Programming entries for
Nadia Boukhelifa
Anastasia Bezerianos
Waldo Cancino
Evelyne Lutton
Citations