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Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures

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Title: Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures
Authors: Elmira Ghoulbeigi
Publication Year: 2010
Subject Terms: Other computer and information sciences, Genetic programming (Computer science), Gene expression, Genetic algorithms -- Mathematical models, Genetic algorithms -- Data processing, Evolutionary programming (Computer science), Evolutionary computation
Description: This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.
Document Type: thesis
Language: unknown
DOI: 10.32920/ryerson.14646798.v1
Availability: https://doi.org/10.32920/ryerson.14646798.v1
https://figshare.com/articles/thesis/Indirect_Estimation_Of_Distribution_Algorithms_For_The_Evolution_Of_Tree-Shaped_Structures/14646798
Rights: In Copyright
Accession Number: edsbas.E2C50244
Database: BASE
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  – Url: https://doi.org/10.32920/ryerson.14646798.v1#
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  Data: Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures
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  Data: <searchLink fieldCode="AR" term="%22Elmira+Ghoulbeigi%22">Elmira Ghoulbeigi</searchLink>
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  Data: 2010
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  Data: <searchLink fieldCode="DE" term="%22Other+computer+and+information+sciences%22">Other computer and information sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+programming+%28Computer+science%29%22">Genetic programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms+--+Mathematical+models%22">Genetic algorithms -- Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms+--+Data+processing%22">Genetic algorithms -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+programming+%28Computer+science%29%22">Evolutionary programming (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+computation%22">Evolutionary computation</searchLink>
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  Data: This thesis explores indirect estimation of distribution algorithms (IEDAs) for the evolution of tree structured expressions. Unlike conventional estimation of distribution algorithms, IEDAs maintain a distribution of the genotype space and indirectly search the solution space by performing a genotype-to-phenotype mapping. In this work we introduce two IEDAs named PDPE and N-gram GEP. PDPE induces a population of programs, encoded as fixed-length gene expression programming (GEP) chromosomes, by iteratively refining and randomly sampling a probability distribution of program instructions. N-gram GEP attempts to capture regularities in GEP chromosomes by sampling the probability distribution of triplet of instructions (3-grams). We tested the performance of these systems using a variety of non-trivial test problems, such as symbolic regression and the lawn-mower problem. We compared PDPE and N-gram GEP with their predecessors, probabilistic incremental program evolution (PIPE) and N-gram GP, and the canonical GEP algorithm. The results proved that our methodology is more efficient than PIPE and the canonical GEP algorithm.
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  Data: 10.32920/ryerson.14646798.v1
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  Data: https://doi.org/10.32920/ryerson.14646798.v1<br />https://figshare.com/articles/thesis/Indirect_Estimation_Of_Distribution_Algorithms_For_The_Evolution_Of_Tree-Shaped_Structures/14646798
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        Value: 10.32920/ryerson.14646798.v1
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      – Text: unknown
    Subjects:
      – SubjectFull: Other computer and information sciences
        Type: general
      – SubjectFull: Genetic programming (Computer science)
        Type: general
      – SubjectFull: Gene expression
        Type: general
      – SubjectFull: Genetic algorithms -- Mathematical models
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      – SubjectFull: Genetic algorithms -- Data processing
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      – SubjectFull: Evolutionary programming (Computer science)
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      – SubjectFull: Evolutionary computation
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      – TitleFull: Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures
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