Limiting the Number of Fitness Cases in Genetic Programming Using Statistics
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- @InProceedings{giacobini:ppsn2002:pp371,
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author = "Mario Giacobini and Marco Tomassini and
Leonardo Vanneschi",
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title = "Limiting the Number of Fitness Cases in Genetic
Programming Using Statistics",
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booktitle = "Parallel Problem Solving from Nature - PPSN VII",
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address = "Granada, Spain",
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month = "7-11 " # sep,
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pages = "371--380",
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year = "2002",
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editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
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number = "2439",
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series = "Lecture Notes in Computer Science",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Parameter
tuning, Fitness Evaluation, Theory of evolutionary
computing, Central Limit Theorem, Entropy",
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ISBN = "3-540-44139-5",
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DOI = "doi:10.1007/3-540-45712-7_36",
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URL = "https://rdcu.be/cJz75",
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size = "10 pages",
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abstract = "Fitness evaluation is often a time consuming activity
in genetic programming applications and it is thus of
interest to find criteria that can help in reducing the
time without compromising the quality of the results.
We use well-known results in statistics and information
theory to limit the number of fitness cases that are
needed for reliable function reconstruction in genetic
programming. By using two numerical examples, we show
that the results agree with our theoretical
predictions. Since our approach is problem-independent,
it can be used together with techniques for choosing an
efficient set of fitness cases.",
- }
Genetic Programming entries for
Mario Giacobini
Marco Tomassini
Leonardo Vanneschi
Citations