Proposal of Surrogate Model for Genetic Programming Based on Program Structure Similarity
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- @InProceedings{Kino:2020:SICE,
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author = "Sohei Kino and Tomohiro Harada and Ruck Thawonmas",
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title = "Proposal of Surrogate Model for Genetic Programming
Based on Program Structure Similarity",
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booktitle = "2020 59th Annual Conference of the Society of
Instrument and Control Engineers of Japan (SICE)",
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year = "2020",
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pages = "808--813",
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abstract = "This paper proposes a novel surrogate model for
genetic programming that estimates the fitness of each
individual by using the tree structure similarity. In
particular, the fitness of each individual is estimated
with the nearest neighbor method by comparing each
individual with the evaluated population. We conduct an
experiment to investigate the effectiveness of the
proposed method. In the experiment, we compare genetic
programming with and without the proposed surrogate
model on the symbolic regression problem. We assess the
convergence speed and the discovery ratio of the
optimum program. The experimental result reveals that
the proposed method improves the convergence speed of
genetic programming while maintaining the discovery
rate of the optimum program.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.23919/SICE48898.2020.9240324",
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month = sep,
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notes = "Also known as \cite{9240324}",
- }
Genetic Programming entries for
Sohei Kino
Tomohiro Harada
Ruck Thawonmas
Citations