Mutation vs. Crossover with Genetic Programming
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- @InProceedings{Ashlock:2006:ANNIEw,
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author = "Wendy Ashlock",
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title = "Mutation vs. Crossover with Genetic Programming",
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booktitle = "ANNIE 2006, Intelligent Engineering Systems through
Artificial Neural Networks",
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year = "2006",
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editor = "Cihan H. Dagli and Anna L. Buczak and
David L. Enke and Mark Embrechts and Okan Ersoy",
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volume = "16",
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address = "St. Louis, MO, USA",
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month = nov # " 5-8",
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note = "Part I: Evolutionary Computation",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "0791802566",
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DOI = "doi:10.1115/1.802566.paper2",
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abstract = "Understanding how variation operators work leads to a
better understanding both of the search space and of
the problem being solved. This study examines the
behaviour of mutation and crossover operators in
genetic programming using parse trees to find solutions
to 3-parity and 4-parity. The standard subtree
crossover and subtree mutation operators are studied
along with two new operators, fold mutation and fusion
crossover. They are studied in terms of how often and
how fast they solve the problem; how much they change
the fitness on average; and what proportion of
variations are neutral, harmful, and helpful. It is
found that operators behave differently when used alone
than when used together with another operator and that
some operators behave differently when solving 3-parity
and when solving 4-parity.",
- }
Genetic Programming entries for
Wendy Ashlock
Citations