Development of unimodal and multimodal optimization algorithms based on multi-gene genetic programming
Created by W.Langdon from
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- @PhdThesis{Povoa:thesis,
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author = "Rogerio {Cortez Brito Leite Povoa}",
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title = "Development of unimodal and multimodal optimization
algorithms based on multi-gene genetic programming",
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school = "Pontificia Universidade Catolica do Rio de Janeiro,
Departamento de Engenharia Eletrica",
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year = "2018",
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address = "Brazil",
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month = mar,
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keywords = "genetic algorithms, genetic programming, MG-PMA,
Numerical Optimization, Multimodal Optimisation,
Evolutionary Computation, Multi-Gene Genetic
Programming",
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URL = "https://www.maxwell.vrac.puc-rio.br/34935/34935.PDF",
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URL = "https://www.maxwell.vrac.puc-rio.br/colecao.php?strSecao=resultado&nrSeq=34935@2",
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DOI = "doi:10.17771/pucrio.acad.34935",
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size = "188 pages",
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abstract = "Genetic programming techniques allow flexibility in
the optimization process, making it possible to use
them in different areas of knowledge and providing new
ways for specialists to advance in their areas more
quickly and more accurately.Parameter mapping approach
is a numerical optimization method that uses genetic
programming to find an appropriate mapping scheme among
initial guesses to optimal parameters for a system.
Although this approach yields good results for problems
with trivial solutions, the use of large
equations/trees may be required to make this mapping
appropriate for more complex systems.In order to
increase the flexibility and applicability of the
method to systems of different levels of complexity,
this thesis introduces a generalization by thus using
multi-gene genetic programming to perform a
multivariate mapping, avoiding large complex
structures.Three sets of benchmark functions, varying
in complexity and dimensionality, were considered.
Statistical analyses carried out suggest that this new
method is more flexible and performs better on average,
considering challenging benchmark functions of
increasing dimensionality.This thesis also presents an
improvement of this new method for multimodal numerical
optimization.This second algorithm uses some niching
techniques based on the clearing procedure to maintain
the population diversity. A multimodal benchmark set
with different characteristics and difficulty levels to
evaluate this new algorithm is used. Statistical
analysis suggested that this new multimodal method
using multi-gene genetic programming can be used for
problems that requires more than a single solution. As
a way of testing real-world problems for these methods,
one application in nanotechnology is proposed in this
thesis: the structural optimization of quantum well
infra-red photo detector from a desired energy.The
results present new structures better than those known
in the literature with improvement of 59.09 percent.",
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notes = "In English.
CEC 2017 Benchmark Multimodal Functions.
Supervisor: Patricia Lustoza de Souza. Co-advisor:
Bruno Araujo Cautiero Horta",
- }
Genetic Programming entries for
Rogerio C B L Povoa
Citations