Population Variation in Canonical Tree-based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @PhdThesis{Kouchakpour:thesis,
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author = "Peyman Kouchakpour",
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title = "Population Variation in Canonical Tree-based Genetic
Programming",
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school = "School of Electrical, Electronic and Computer
Engineering, University of Western Australia",
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year = "2008",
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address = "Perth, Australia",
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month = May,
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keywords = "genetic algorithms, genetic programming, Population
genetics, Genetic programming (Computer science),
Population variation, Computational effort,
Evolutionary algorithms, Fibonacci Sequence Induction",
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URL = "http://robotics.ee.uwa.edu.au/theses/2008-Genetic-Kouchakpour-PhD.pdf",
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URL = "https://research-repository.uwa.edu.au/en/publications/population-variation-in-canonical-tree-based-genetics-programming",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.9061",
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size = "284 pages",
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abstract = "The Genetic Programming paradigm, which applies the
Darwinian principle of evolution to hierarchical
computer programs, has produced promising breakthroughs
in various scientific and engineering applications.
However, one of the main drawbacks of Genetic
Programming has been the often large amount of
computational effort required to solve complex
problems. There have been various amounts of research
conducted to devise innovative methods to improve the
efficiency of Genetic Programming. This thesis has
three main contributions. It firstly provides a
comprehensive overview of the related work to improve
the performance of Genetic Programming and classifies
these various proposed approaches into categories.
Secondly, a new static population variation scheme (PV)
is proposed, whereby the size of the population is
varied according to a predetermined schedule during the
execution of the Genetic Programming system with the
aim of reducing the computational effort with respect
to that of Standard Genetic Programming. Within this
new static scheme the initial population size is made
to be different from the initial size of the Standard
Genetic Programming such that the worst case
computational effort is never greater than that of the
Standard Genetic Programming. Various static schemes
for altering population size under this proposal are
investigated using a comprehensive range of standard
problems to determine whether the nature of the
'population variation', i.e. the way the population is
varied during the search, has any significant impact on
Genetic Programming performance. It is shown that these
population variation schemes do have the capacity to
provide solutions at a lower computational cost
compared with the Standard Genetic Programming.
Thirdly, three innovations for dynamically varying the
population size during the run of the Genetic
Programming system are proposed. These are related to
what is called Dynamic Population Variation (DPV),
where the size of the population is dynamically varied
using a heuristic feedback mechanism during the
execution of the Genetic Programming with the aim of
reducing the computational effort. The efficacy of
these innovations is examined using the same
comprehensive range of standard representative
problems. It is shown that these new ideas do have the
capacity to provide solutions at a lower computational
cost compared with standard genetic programming and
previously reported algorithms. Finally, further
interesting research potentials for population
variation are identified together with some of the open
areas of research within the Genetic Programming and
also possible future trends in this discipline.",
- }
Genetic Programming entries for
Peyman Kouchakpour
Citations