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Visual and Audio Monitoring of Island Based Parallel Evolutionary Algorithms

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Abstract

Monitoring and visualisation tools are currently attracting more and more attention in order to understand how search spaces are explored by complex optimisation ecosystems such as parallel evolutionary algorithms based on island models. Multilevel visualisation is actually a desirable feature for facilitating the monitoring of computationally expensive runs involving several hundreds of computers during hours or even days. In this paper we present two components of a future multilevel monitoring system: MusEAc, a high level, audio monitoring allowing to listen to a run and tune it in real time and GridVis, a lower lever, more precise a posteriori visualisation tool that lets the user understand why the algorithm has performed well or bad.

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Authors and Affiliations

  1. INRA, UMR GMPA, 1 Av. Brétignières, 78850, Thiverval-Grignon, France

    Evelyne Lutton

  2. ENSTA ParisTech, 828, Boulevard des Maréchaux, 91762, Palaiseau Cedex, France

    Hugo Gilbert

  3. INRIA Saclay-Ile-de-France, AVIZ team, Bâtiment 660, 91405, Orsay Cedex, France

    Waldo Cancino & Benjamin Bach

  4. ICube laboratory, and ECCE e-laboratory, CS-DC UNESCO UniTwin, Strasbourg University, 7, rue René Descartes, 67084, Strasbourg, France

    Joseph Pallamidessi, Pierre Parrend & Pierre Collet

  5. ECAM Strasbourg-Europe 2, Rue de Madrid, Schiltigheim, CS 20013, 67012, Strasbourg Cedex, France

    Pierre Parrend

Authors
  1. Evelyne Lutton
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  2. Hugo Gilbert
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  3. Waldo Cancino
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  4. Benjamin Bach
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  5. Joseph Pallamidessi
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  6. Pierre Parrend
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  7. Pierre Collet
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Correspondence to Evelyne Lutton.

Additional information

This work has been funded by the French National Agency for research (ANR), under the grant ANR-11-EMMA-0017, EASEA-Cloud Emergence project 2011, http://www.agence-nationale-recherche.fr/.

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Lutton, E., Gilbert, H., Cancino, W. et al. Visual and Audio Monitoring of Island Based Parallel Evolutionary Algorithms. J Grid Computing 13, 309–327 (2015). https://doi.org/10.1007/s10723-014-9321-8

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