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2017 | OriginalPaper | Chapter

Comparison of Parallel Linear Genetic Programming Implementations

Authors : David Grochol, Lukas Sekanina

Published in: Recent Advances in Soft Computing

Publisher: Springer International Publishing

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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.

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Title: Comparison of Parallel Linear Genetic Programming Implementations
Authors: David Grochol
Lukas Sekanina
Publisher: Springer International Publishing
Book: Recent Advances in Soft Computing
Print ISBN: 978-3-319-58087-6

Electronic ISBN: 978-3-319-58088-3

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-58088-3_7

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