Improving Convergence in Cartesian Genetic Programming Using Adaptive Crossover, Mutation and Selection
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- @InProceedings{Kalkreuth:2015:ieeeSSCI,
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author = "Roman Kalkreuth and Guenter Rudolph and Jorg Krone",
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booktitle = "2015 IEEE Symposium Series on Computational
Intelligence",
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title = "Improving Convergence in Cartesian Genetic Programming
Using Adaptive Crossover, Mutation and Selection",
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year = "2015",
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pages = "1415--1422",
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abstract = "Genetic programming (GP) can be described as a
paradigm which opens the automatic derivation of
programs for problem solving. GP as popularized by Koza
uses tree representation. The application of GP takes
place on several types of complex problems and became
very important for Symbolic Regression. Miller and
Thomson introduced a new directed graph representation
called Cartesian Genetic Programming (CGP). We use this
representation for very complex problems. CGP enables a
new application on classification and image processing
problems. Previous research showed that CGP has a low
convergence rate on complex problems. Like in other
approaches of evolutionary computation, premature
convergence is also a common issue. Modern GP systems
produce population statistics in every iteration. In
this paper we introduce a new adaptive strategy which
uses population statistics to improve the convergence
of CGP. A new metric for CGP is introduced to classify
the healthy population diversity. Our strategy
maintains population diversity by adapting the
probabilities of the genetic operators and selection
pressure. We demonstrate our strategy on several
regression problems and compare it to the traditional
algorithm of CGP. We conclude this paper by giving
advices about parametrisation of the adaptive
strategy.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI.2015.201",
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month = dec,
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notes = "Also known as \cite{7376777}",
- }
Genetic Programming entries for
Roman Tobias Kalkreuth
Guenter Rudolph
Jorg Krone
Citations