Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Indirect Estimation Of Distribution Algorithms For The Evolution Of Tree-Shaped Structures |
| Συγγραφείς: |
Ghoulbeigi, Elmira (Author) |
| Συνεισφορές: |
Ryerson University (Degree granting institution) |
| Έτος έκδοσης: |
2013 |
| Συλλογή: |
Ryerson University: RULA Digital Repository |
| Θεματικοί όροι: |
Genetic programming (Computer science), Gene expression, Genetic algorithms -- Mathematical models, Genetic algorithms -- Data processing, Evolutionary programming (Computer science), Evolutionary computation |
| Περιγραφή: |
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. |
| Τύπος εγγράφου: |
thesis |
| Γλώσσα: |
English |
| Διαθεσιμότητα: |
https://digital.library.ryerson.ca/islandora/object/RULA%3A1723 |
| Αριθμός Καταχώρησης: |
edsbas.87EF6755 |
| Βάση Δεδομένων: |
BASE |