Extended Lock-in Feedback Applicable on Higher Dimensional Function Maximization
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2016-02-19
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en
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This thesis introduces two novel versions of the existing Lockin
Feedback algorithm. This algorithm is a means of performing
stochastic optimization. The novel versions include
alterations that make them applicable on higher dimensional
function maximization problems. By running several simulation
tests and examining the cumulative regret returned by
each method, this thesis shows that the proposed extensions
prove to be performing well on function maximization problems
of two dimensions. Both versions are also applied on
a function containing multiple maxima, in order to test their
ability to deal with more complex maximization problems.
By making adjustments to the Lock-in Feedback algorithm
inspired on the Artificial Bee Colony algorithm, it was made
sure that the method would uncover global in stead of local
maxima.
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Faculteit der Sociale Wetenschappen