Towards a Multi-Output Kaizen Programming Algorithm
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- @InProceedings{Ferreira:2021:LA-CCI,
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author = "Jimena Ferreira and Ana Ines Torres and
Martin Pedemonte",
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title = "Towards a Multi-Output Kaizen Programming Algorithm",
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booktitle = "2021 IEEE Latin American Conference on Computational
Intelligence (LA-CCI)",
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year = "2021",
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month = nov,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/LA-CCI48322.2021.9769841",
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abstract = "A model obtained from solving a symbolic regression
problem is a surrogate model that represent a system
with high accuracy. In the area of process system
engineering, surrogate models substitute rigorous
models in optimization and design process problems. As
chemical processes have several outputs with a common
physical-chemical phenomena, it is expected that the
surrogate models generated for the outputs share terms
or function basis. Kaizen Programming (KP) is a novel
technique to solve symbolic regression problems, which
do not assume any supposition of the form of the model
in advance. This technique has shown a better
performance than Genetic Programming on benchmarking
functions. we propose an extension of Kaizen
Programming, Multi-Output KP (MO-KP), to construct
multi-output models in a single execution.The
experimental evaluation was conducted on an extension
of three classical benchmarking functions to
multi-output scenarios, considering three different
schemes of function basis sharing. The experimental
results shown that MO-KP builds well fitted models, and
it is even able to construct better models than
single-output KP in some scenarios. The results also
confirm that MO-KP favors the sharing of terms between
the generated models. Finally, we found that the median
execution time of MO-KP is in general shorter than the
equivalent executions of single-output KP, but with
larger variability in the distribution of the
runtimes.",
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notes = "Also known as \cite{9769841}",
- }
Genetic Programming entries for
Jimena Ferreira
Ana Ines Torres
Martin Pedemonte
Citations