Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Iqbal:2013:ieeeTEC,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Reusing Building Blocks of Extracted Knowledge to
Solve Complex, Large-Scale Boolean Problems",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2014",
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volume = "18",
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number = "4",
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pages = "465--480",
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month = aug,
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keywords = "genetic algorithms, genetic programming, XCS, Learning
Classifier Systems, Layered Learning, Scalability,
Building Blocks, Code Fragments",
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ISSN = "1089-778X",
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URL = "http://homepages.ecs.vuw.ac.nz/~mengjie/papers/",
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URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06595603",
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DOI = "doi:10.1109/TEVC.2013.2281537",
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size = "16 pages",
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abstract = "Evolutionary computation techniques have had limited
capabilities in solving large-scale problems due to the
large search space demanding large memory and much
longer training times. In the work presented here, a
genetic programming like rich encoding scheme has been
constructed to identify building blocks of knowledge in
a learning classifier system. The fitter building
blocks from the learning system trained against smaller
problems have been used in a higher complexity problem
in the domain in order to achieve scalable learning.
The proposed system has been examined and evaluated on
four different Boolean problem domains, i.e.
multiplexer, majority-on, carry, and even-parity
problems. The major contribution of this work is to
successfully extract useful building blocks from
smaller problems and reuse them to learn more complex,
large-scale problems in the domain, e.g. 135-bits
multiplexer problem, where the number of possible
instances is 2**135 = 4.0 10**40, is solved by reusing
the extracted knowledge from the learnt lower level
solutions in the domain. Autonomous scaling is, for the
first time, shown to be possible in learning classifier
systems. It improves effectiveness and reduces the
number of training instances required in large
problems, but requires more time due to its sequential
build-up of knowledge.",
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notes = "Entered for 2013 HUMIES GECCO 2013
",
- }
Genetic Programming entries for
Muhammad Iqbal
Will N Browne
Mengjie Zhang
Citations