Influence of Two Different Crossover Operators Use Onto GPA Efficiency
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- @InProceedings{Brandejsky:2018:ICCAIRO,
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author = "Tomas Brandejsky",
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booktitle = "2018 International Conference on Control, Artificial
Intelligence, Robotics Optimization (ICCAIRO)",
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title = "Influence of Two Different Crossover Operators Use
Onto {GPA} Efficiency",
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year = "2018",
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pages = "127--132",
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abstract = "Increasing capabilities of today computers, especially
size of memory and computational power open new
application areas to Genetic Programming Algorithms
[1]. Unfortunately, efficiency of these algorithms is
not big and decreases with solved problem complexity.
Thus, its increase is extremely important for opening
of new application domains. There exists three main
areas that should potentially influence GPA efficiency.
They are algorithms, pseudo-random number generator
behaviours and evolutionary operators. Genetic
programming algorithms use two basic evolutionary
operators - mutation and crossover in the sense of
Darwinian evolution. Non-looking to the fact, that it
is possible to define additional operators like e.g.
application defined operators [2], there are many
different implementations of both basic evolutionary
operators [3] and each of them is sometimes useful in
artificial evolutionary process. Thus, the main
question solved in this paper is that it might bring
some advance to use two randomly executed different
crossover operators in GPA. The study is focused to
symbolic regression problem and as GPA is used GPA-ES,
because it is capable to eliminate influence of
solution parameters (constants) identification and thus
to produce more clear results.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ICCAIRO.2018.00029",
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month = may,
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notes = "Also known as \cite{8698424}",
- }
Genetic Programming entries for
Tomas Brandejsky
Citations