Improving Generalisation of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators
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- @Article{Chen:ieeeTEC:8462796,
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author = "Qi Chen and Bing Xue and Mengjie Zhang",
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title = "Improving Generalisation of Genetic Programming for
Symbolic Regression with Angle-Driven Geometric
Semantic Operators",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2019",
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volume = "23",
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number = "3",
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pages = "488--502",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Geometric
Semantic Operator, Symbolic Regression,
Generalisation",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2018.2869621",
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size = "15 pages",
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abstract = "Geometric semantic genetic programming has recently
attracted much attention. The key innovations are
inducing a unimodal fitness landscape in the semantic
space and providing a theoretical framework for
designing geometric semantic operators. The geometric
semantic operators aim to manipulate the semantics of
programs by making a bounded semantic impact and
generating child programs with similar or better
behaviour than their parents. These properties are
shown to be highly related to a notable generalisation
improvement in genetic programming. However, the
potential ineffectiveness and difficulties in bounding
the variations in these geometric operators still
limits their positive effect on generalisation. This
work attempts to further explore the geometry and
search space of geometric operators to gain a greater
generalisation improvement in genetic programming for
symbolic regression. To this end, a new angle-driven
selection operator and two new angle-driven geometric
search operators are proposed. The angle-awareness
brings new geometric properties to these geometric
operators, which are expected to provide a greater
leverage for approximating the target semantics in each
operation, and more importantly, be resistant to over
fitting. The experiments show that compared with two
state-of-the-art geometric semantic operators, our
angle-driven geometric operators not only drive the
evolutionary process to fit the target semantics more
efficiently but also improve the generalisation
performance. A further comparison between the evolved
models shows that the new method generally produces
simpler models with a much smaller size and is more
likely to evolve towards the correct structure of the
target models.",
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notes = "also known as \cite{8462796}",
- }
Genetic Programming entries for
Qi Chen
Bing Xue
Mengjie Zhang
Citations