Publication Details
Comparison of Parallel Linear Genetic Programming Implementations
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Linear genetic programming, parallel implementation, island model, hash function,
symbolic regresion
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
parallel implementations of LGP suitable for current multi-core processors. The
implementations are based on a parallel evaluation of candidate programs and the
island model of parallel evolutionary algorithm in which subpopulations are
evolved independently, but some genetic material can be exchanged by means of
migration. Proposed implementations are evaluated using three symbolic regression
problems and hash function design problem.
@inproceedings{BUT144385,
author="David {Grochol} and Lukáš {Sekanina}",
title="Comparison of Parallel Linear Genetic Programming Implementations",
booktitle="Recent Advances in Soft Computing: Proceedings of the 22nd International Conference on Soft Computing (MENDEL 2016) held in Brno, Czech Republic, at June 8-10, 2016",
year="2017",
pages="64--76",
publisher="Springer International Publishing",
address="Cham",
doi="10.1007/978-3-319-58088-3\{_}7",
isbn="978-3-319-58088-3",
url="https://www.fit.vut.cz/research/publication/10997/"
}