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Comparison of Parallel Linear Genetic Programming Implementations

  • David Grochol
  • Lukas Sekanina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 576)

Abstract

Linear genetic programming (LGP) represents candidate programs as sequences of instructions for a register machine. In order to accelerate the evaluation time of candidate programs and reduce the overall time of evolution, we propose various parallel implementations of LGP suitable for the current multi-core processors. The implementations are based on a parallel evaluation of candidate programs and the island model of the parallel evolutionary algorithm in which the subpopulations are evolved independently, but some genetic material can be exchanged by means of the migration. Proposed implementations are evaluated using three symbolic regression problems and a hash function design problem.

Keywords

Hash Function Fitness Evaluation Single Instruction Multiple Data Island Model Chunk Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by Brno University of Technology project FIT-S-14-2297 and The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II); project IT4Innovations excellence in science - LQ1602.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Information Technology, IT4Innovations Centre of ExcellenceBrno University of TechnologyBrnoCzech Republic

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