More Efficient Evolution of Small Genetic Programs in Cartesian Genetic Programming by Using Genotypic Age
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- @InProceedings{Kalkreuth:2016:cec,
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author = "Roman Kalkreuth and Guenter Rudolph and Joerg Krone",
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title = "More Efficient Evolution of Small Genetic Programs in
Cartesian Genetic Programming by Using Genotypic Age",
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booktitle = "2016 IEEE Congress of Evolutionary Computation",
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year = "2016",
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editor = "Yun Li",
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pages = "5052--5059",
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address = "Vancouver",
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month = "25-29 " # jul,
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publisher = "IEEE",
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email = "roman.kalkreuth@tu-dortmund.de",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7748330",
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abstract = "Genetic Programming as an automated method to evolve
suitable computer programs for a predefined task can
also be applied to multi-objective optimization
problems. Originally, Genetic Programming uses tree
structures for the representation of a computer
program, but further development also enabled a graph
based representation called Cartesian Genetic
Programming. In the last years, Cartesian Genetic
Programming has also been applied to multi-objective
optimization problems. For example, we use this
representation to determine smaller mathematical
expressions or image processing filters with a maximum
number of operators. Previous research showed that
algorithm stagnation is a common issue in Cartesian
Genetic Programming. This behaviour comes along with a
decrease of diversity in the population and increases
the computational effort to find a suitable solution.
In this paper, we combine the multi-objective search
for smaller genetic programs with an efficient
diversity preservation technique. A modified version of
the popular NSGA-II algorithm is presented to evolve
small programs with a lower amount of fitness
evaluations and a higher success rate.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Roman Tobias Kalkreuth
Guenter Rudolph
Joerg Krone
Citations