Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression
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- @Article{Qi_Chen:ieeeTEC,
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author = "Qi Chen and Bing Xue and Mengjie Zhang",
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title = "Preserving Population Diversity Based on Transformed
Semantics in Genetic Programming for Symbolic
Regression",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2021",
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volume = "25",
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number = "3",
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pages = "433--447",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Population
Diversity, Symbolic Regression",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2020.3046569",
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size = "15 pages",
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abstract = "Population diversity plays an important role in
avoiding premature convergence in evolutionary
techniques including genetic programming. Obtaining an
adequate level of diversity during the evolutionary
process has became a concern of many previous
researches in genetic programming. This work proposes a
new novelty metric for entropy based diversity measure
for genetic programming. The new novelty metric is
based on the transformed semantics of models in genetic
programming, where the semantics are the set of outputs
of a model on the training data and principal component
analysis is used for a transformation of the semantics.
Based on the new novelty metric, a new diversity
preserving framework, which incorporates a new fitness
function and a new selection operator, is proposed to
help genetic programming achieve a good balance between
the exploration and the exploitation, thus enhancing
its learning and generalisation performance. Compared
with two stat-of-the-art diversity preserving methods,
the new method can generalise better and reduce the
overfitting trend more effectively in most cases.
Further examinations on the properties of the search
process confirm that the new framework notably enhances
the evolvability and locality of genetic programming.",
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notes = "also known as \cite{9302659}
Evolutionary Computation Research Group at the School
of Engineering and Computer Science, Victoria
University of Wellington, Wellington 6140, New
Zealand.",
- }
Genetic Programming entries for
Qi Chen
Bing Xue
Mengjie Zhang
Citations