dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Thierens, D. | |
dc.contributor.advisor | Vreeswijk, G.A.W. | |
dc.contributor.author | Berg, A. van den | |
dc.date.accessioned | 2013-10-07T17:01:02Z | |
dc.date.available | 2013-10-07 | |
dc.date.available | 2013-10-07T17:01:02Z | |
dc.date.issued | 2013 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/15107 | |
dc.description.abstract | There are different ways to obtain a good Artificial Neural Network. When
training, the choice of the data set is of importance to the quality of the
resulting network. When evolving a network using Genetic Algorithms, it is
important that the representation of the network does not interfere with
the passing-on of information to next generations. I looked into the effects
of data representation on the quality of the trained networks, and I
investigated one solution proposed by Thierens (1996) to unheuristically
remove redundancies in genotype. I could not verify the results found in
the proposed solution. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 296576 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | The Effects of Problem Representation and Network Representation on Training Results of Artificial Neural Networks | |
dc.type.content | Bachelor Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.courseuu | Kunstmatige Intelligentie | |