Benchmarking an artificial neural network tuned by a genetic algorithm
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
This thesis starts with a brief introduction to neural networks and the tuning of neural networks using genetic algorithms. An improved genetic algorithm is benchmarked using the technical paper Proben1 as a starting point. The benefits of using a genetic algorithm as well as results of the benchmark tests in comparison to a resilient backpropagation algorithm are discussed. The improved genetic algorithm is not a universal solution to all classification problems. Even though it outperforms the resilient backpropagation algorithm slightly in these benchmark tests more benchmarking on more vast solution domains must be performed.
Place, publisher, year, edition, pages
, UMNAD, 908
Engineering and Technology
IdentifiersURN: urn:nbn:se:umu:diva-58253OAI: oai:DiVA.org:umu-58253DiVA: diva2:547519
Bachelor of Science Programme in Computing Science