2 research outputs found
Stochastic and deterministic algorithms for continuous black-box optimization
Continuous optimization is never easy: the exact solution
is always a luxury demand and the theory of it is not always analytical and
elegant. Continuous optimization, in practice, is essentially about the
efficiency: how to obtain the solution with same quality using as minimal
resources (e.g., CPU time or memory usage) as possible? In this thesis, the
number of function evaluations is considered as the most important resource
to save. To achieve this goal, various efforts have been implemented and
applied successfully. One research stream focuses on the so-called stochastic
variation (mutation) operator, which conducts an (local) exploration of the
search space. The efficiency of those operator has been investigated closely,
which shows a good stochastic variation should be able to generate a good
coverage of the local neighbourhood around the current search solution. This
thesis contributes on this issue by formulating a novel stochastic variation
that yields good space coverage.
Algorithms and the Foundations of Software technolog