Hierachical Processing for Evolving Recursive and Modular Programs Using Higher Order Functions and Lambda Abstractions
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- @Article{TinaYu:2001:GPEM,
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author = "Tina Yu",
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title = "Hierachical Processing for Evolving Recursive and
Modular Programs Using Higher Order Functions and
Lambda Abstractions",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2001",
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volume = "2",
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number = "4",
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pages = "345--380",
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month = dec,
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keywords = "genetic algorithms, genetic programming, hierarchical
processing, recursion, structure abstraction,
higher-order functions, lambda abstraction,
polymorphism, type systems",
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ISSN = "1389-2576",
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URL = "http://www.cs.mun.ca/~tinayu/Publications_files/gpem.pdf",
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DOI = "doi:10.1023/A:1012926821302",
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size = "36 pages",
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abstract = "We present a novel approach using higher-order
functions and l abstraction to evolve recursive and
modular programs. Moreover, a new term 'structure
abstraction' is introduced to describe the property
emerged from the higher-order function program
structure. We test this technique on the general
even-parity problem. The results indicate that this
approach is very effective with the general even-parity
problem due to the appropriate selection of the foldr
higher-order function. Initially, foldr structure
abstraction identify the promising area of the search
space at generation zero. Once the population is within
the promising area, foldr structure abstraction
provides hierarchical processing for search.
Consequently, solutions to the general even-parity
problem are found very efficiently. We identify the
limitations of this new approach and conclude that only
when the appropriate higher-order function is selected
that the benefits of structure abstraction show.",
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notes = "STGP polymorphic general solution to even-n-parity
from 12 test cases.
Article ID: 386362",
- }
Genetic Programming entries for
Tina Yu
Citations