Inferring Phylogenies Using Evolutionary Algorithms: A maximum likelihood approach for constructing phylogenetic trees from molecular data
This thesis has evaluated the use of the computationally expensive
maximum-likelihood (ML) method coupled with an evolutionary
algorithm (EA) for the problem of inferring evolutionary
relationships among species (phylogenies) from molecular data. ML
methods allow using all the information from molecular data, such
as DNA sequences, and have several beneficial properties compared to
other methods. Evolutionary algorithms is a class of optimization
algorithms that often perform well in complex fitness landscapes.
EAs are also proclaimed to be easy to parallelize, an aspect that
is increasingly more important.
A parallel EA system has been implemented and tested on a cluster
for the task of phylogeny inference. The system shows promising
results and is able to utilize processors of a massively parallel
system in a transparent manner.
Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2011. , 93 p.
ntnudaim:6084, MTDT datateknikk, Intelligente systemer
IdentifiersURN: urn:nbn:no:ntnu:diva-13687Local ID: ntnudaim:6084OAI: oai:DiVA.org:ntnu-13687DiVA: diva2:441755
Downing, Keith, Professor