Attributed Grammatical Evolution with Lookahead for the Multiple Knapsack Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Karim:2012:MC,
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author = "Muhammad Rezaul Karim and Conor Ryan",
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title = "Attributed Grammatical Evolution with Lookahead for
the Multiple Knapsack Problem",
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journal = "Memetic Computing",
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year = "2012",
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volume = "4",
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number = "4",
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pages = "279--302",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Attribute Grammar, Multiple Knapsack
Problem",
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ISSN = "1865-9284",
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publisher = "Springer-Verlag",
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language = "English",
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DOI = "doi:10.1007/s12293-012-0097-8",
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size = "24 pages",
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abstract = "This paper presents an Attribute Grammar with
Lookahead (AG+LA) approach, a technique to solve
heavily constrained Multiple Knapsack Problem. This
approach incorporates a form of look ahead into the
mapping process of Grammatical Evolution (GE) using
Attribute Grammar (AG) to focus only on feasible
solutions, thereby avoiding issues such as repeated
remapping and introns, both of which are limitations of
previous approaches based on AG. We also present AG+LAE
(AG+LA with an efficiency measure to bias the search
towards the most efficient, i.e., best value, objects),
the successor of AG+LA where a biasing process is
incorporated using problem specific knowledge to
significantly improve the performance of its
predecessor, both in terms of the number of evaluations
required and the quality of solutions obtained.
Degenerate code, as used in DNA, is code that uses
redundancy, so that different codons can represent the
same thing. Many developmental systems, such as GE, use
a degenerate encoding to help promote neutral
mutations, that is, minor genetic changes that do not
result in a phenotypic change. While early work in GE
suggested that some level of degeneracy was important,
it does come at the cost of increasing the size of the
search space. Duplicate Elimination techniques, as
opposed to degenerate encoding, are employed in
decoder-based Evolutionary Algorithms to ensure that
the newly generated solutions are not already contained
in the current population. The results and analysis
show that it is crucial to incorporate duplicate
elimination to improve the performance of both
approaches, while the reduced level of degeneracy is
crucial only for AG+LA.",
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notes = "special issue on the NICSO 2011 workshop",
- }
Genetic Programming entries for
Muhammad Rezaul Karim
Conor Ryan
Citations