El-Omari, Jawad A. (2013) Efficient learning methods to tune algorithm parameters. PhD thesis, University of Warwick.

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Abstract

This thesis focuses on the algorithm configuration problem. In particular, three efficient
learning configurators are introduced to tune parameters offline. The first looks into metaoptimization,
where the algorithm is expected to solve similar problem instances within
varying computational budgets. Standard meta-optimization techniques have to be repeated
whenever the available computational budget changes, as the parameters that work well for
small budgets, may not be suitable for larger ones. The proposed Flexible Budget method
can, in a single run, identify the best parameter setting for all possible computational
budgets less than a specified maximum, without compromising solution quality. Hence, a lot
of time is saved. This will be shown experimentally. The second regards Racing algorithms
which often do not fully utilize the available computational budget to find the best parameter
setting, as they may terminate whenever a single parameter remains in the race. The
proposed Racing with reset can overcome this issue, and at the same time adapt Racing’s
hyper-parameter α online. Experiments will show that such adaptation enables the algorithm
to achieve significantly lower failure rates, compared to any fixed α set by the user. The
third extends on Racing with reset by allowing it to utilize all the information gathered
previously when it adapts α, it also permits Racing algorithms in general to intelligently
allocate the budget in each iteration, as opposed to equally allocating it. All developed
Racing algorithms are compared to two budget allocators from the Simulation Optimization
literature, OCBA and CBA, and to equal allocation to demonstrate under which conditions
each performs best in terms of minimizing the probability of incorrect selection.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HG Finance H Social Sciences > HJ Public Finance Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Algorithms, Parameter estimation, Budget -- Mathematical models, Mathematical optimization
Official Date:	October 2013
Institution:	University of Warwick
Theses Department:	Warwick Business School
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Branke, Jürgen, 1969-
Extent:	xxiii, 218 leaves : charts.
Language:	eng

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