poster

Genetic algorithms and grid computing for artificial embryogeny

Authors:

Sylvain Cussat-Blanc,

Fabien Viale,

Herve Luga,

Yves Duthen,

Denis CaromelAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 281 - 282

https://doi.org/10.1145/1389095.1389139

Published: 12 July 2008 Publication History

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Abstract

Genetic algorithms are very demanding in terms of computing time and, when the population size is large, they need days to complete or even fail due to memory restrictions. It is particularly the case for artificial life where each evaluation can take more than one minute to develop an artificial creature, plant or organism. Indeed, creatures are developed in physical and chemical simulators that require important computation resources. In order to create more and more realistic creatures, we propose a grid parallelized version of genetic algorithms. Two possibilities exist to increase them: supercomputers or computational grids. Because of their scalability, we choose computational grid in their works.

References

[1]

F. Baude, D. Caromel, L. Mestre, F. Huet, and J. Vayssière. Interactive and descriptor-based deployment of object-oriented grid applications. In High Performance Distributed Computing, 2002.

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[2]

R. Bianchini and C. Brown. Parallel genetic algorithms on distributed-memory architectures. Technical Report (revised version), University of Rochester, May 1993.

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[3]

E. Cantu-Paz. A survey of parallel genetic algorithms. Technical report 95004, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1997.

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[4]

S. Cussat-Blanc, H. Luga, and Y. Duthen. Artificial Embryogeny and Grid Computing. Technical Report IRIT/RR-2008-10-FR, IRIT, 2008.

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Deepan Babu PAmudha T(2013)A Novel Genetic Algorithm for Effective Job Scheduling in Grid EnvironmentComputational Intelligence, Cyber Security and Computational Models10.1007/978-81-322-1680-3_42(385-393)Online publication date: 27-Nov-2013
https://doi.org/10.1007/978-81-322-1680-3_42
Cussat-Blanc SLuga HDuthen Y(2009)Cell2OrganProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689959(2708-2715)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689959
Cussat-Blanc SLuga HDuthen Y(2009)Cell2Organ: Self-repairing artificial creatures thanks to a healthy metabolism2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983282(2708-2715)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983282

Index Terms

Genetic algorithms and grid computing for artificial embryogeny
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Deepan Babu PAmudha T(2013)A Novel Genetic Algorithm for Effective Job Scheduling in Grid EnvironmentComputational Intelligence, Cyber Security and Computational Models10.1007/978-81-322-1680-3_42(385-393)Online publication date: 27-Nov-2013
https://doi.org/10.1007/978-81-322-1680-3_42
Cussat-Blanc SLuga HDuthen Y(2009)Cell2OrganProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689959(2708-2715)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689959
Cussat-Blanc SLuga HDuthen Y(2009)Cell2Organ: Self-repairing artificial creatures thanks to a healthy metabolism2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983282(2708-2715)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983282

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A dynamic-balanced scheduler for genetic algorithms for grid computing

Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

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MGC middleware for grid computing: the Globus Toolkit

A dynamic-balanced scheduler for genetic algorithms for grid computing

Efficient Hierarchical Parallel Genetic Algorithms using Grid computing

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