Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data

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

@InCollection{Hodjat:2013:GPTP,
  author =       "Babak Hodjat and Erik Hemberg and Hormoz Shahrzad and 
                 Una-May O'Reilly",
  title =        "Maintenance of a Long Running Distributed Genetic
                 Programming System for Solving Problems Requiring Big
                 Data",
  booktitle =    "Genetic Programming Theory and Practice XI",
  year =         "2013",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Jason H. Moore and Mark Kotanchek",
  publisher =    "Springer",
  chapter =      "4",
  pages =        "65--83",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier system, Cloud scale, Distributed, Big data",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_4",
  abstract =     "We describe a system, ECStar, that outstrips many
                 scaling aspects of extant genetic programming systems.
                 One instance in the domain of financial strategies has
                 executed for extended durations (months to years) on
                 nodes distributed around the globe. ECStar system
                 instances are almost never stopped and restarted,
                 though they are resource elastic. Instead they are
                 interactively redirected to different parts of the
                 problem space and updated with up-to-date learning.
                 Their non-reproducibility (i.e. single play of the tape
                 process) due to their complexity makes them similar to
                 real biological systems. In this contribution we focus
                 upon how ECStar introduces a provocative, important,
                 new paradigm for GP by its sheer size and complexity.
                 ECStar's scale, volunteer compute nodes and distributed
                 hub-and-spoke design have implications on how a
                 multi-node instance is managed. We describe the set up,
                 deployment, operation and update of an instance of such
                 a large, distributed and long running system. Moreover,
                 we outline how ECStar is designed to allow manual
                 guidance and re-alignment of its evolutionary search
                 trajectory.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2013:GPTP} published after the
                 workshop in 2013",
}

Genetic Programming entries for Babak Hodjat Erik Hemberg Hormoz Shahrzad Una-May O'Reilly

Citations