Crossover-Based Tree Distance in Genetic Programming
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- @Article{Gustafson:2008:TEC,
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title = "Crossover-Based Tree Distance in Genetic Programming",
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author = "Steven Gustafson and Leonardo Vanneschi",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2008",
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month = aug,
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volume = "12",
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number = "4",
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pages = "506--524",
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, trees (mathematics)crossover-based tree
distance, distance metrics, evolutionary algorithms,
fitness sharing algorithm, fitness-distance
correlation, genetic programming syntax trees",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2008.915993",
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size = "19 pages",
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abstract = "In evolutionary algorithms, distance metrics between
solutions are often useful for many aspects of guiding
and understanding the search process. A good distance
measure should reflect the capability of the search: if
two solutions are found to be close in distance, or
similarity, they should also be close in the search
algorithm sense, i.e., the variation operator used to
traverse the search space should easily transform one
of them into the other. This paper explores such a
distance for genetic programming syntax trees. Distance
measures are discussed, defined and empirically
investigated. The value of such measures is then
validated in the context of analysis (fitness-distance
correlation is analyzed during population evolution) as
well as guiding search (results are improved using our
measure in a fitness sharing algorithm) and diversity
(new insights are obtained as compared with standard
measures).",
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notes = "also known as \cite{4459225}",
- }
Genetic Programming entries for
Steven M Gustafson
Leonardo Vanneschi
Citations