Genetic algorithms and grid computing for artificial embryogeny

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: Françoise Baude , Denis Caromel , Fabrice Huet , Lionel Mestre , Julien Vayssière, Interactive and Descriptor-Based Deployment of Object-Oriented Grid Applications, Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, p.93, July 24-26, 2002
• 2: Ricardo Bianchini , Christopher M. Brown, Parallel Genetic Algorithms on Distributed-Memory Architectures, University of Rochester, Rochester, NY, 1993
• 3: E. Cantu-Paz. A survey of parallel genetic algorithms. Technical report 95004, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1997.
• 4: S. Cussat-Blanc, H. Luga, and Y. Duthen. Artificial Embryogeny and Grid Computing. Technical Report IRIT/RR-2008-10-FR, IRIT, 2008.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms, Experimentation

Keywords:
artificial embryogeny, genetic algorithms, grid computing