minor corrections

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Nicole Dresselhaus 2017-10-12 11:45:56 +02:00
parent fc0c67f538
commit 68e162a4a1
Signed by: Drezil
GPG Key ID: 057D94F356F41E25

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@ -10,7 +10,7 @@ chapter.
Unless otherwise noted the following holds:
- lowercase letters $x,y,z$
refer to real variables and represent a point in 3D--Space.
refer to real variables and represent the coordinates of a point in 3D--Space.
- lowercase letters $u,v,w$
refer to real variables between $0$ and $1$ used as coefficients in a 3D
B--Spline grid.
@ -26,14 +26,14 @@ Unless otherwise noted the following holds:
\improvement[inline]{Mehr Bilder}
Many modern industrial design processes require advanced optimization methods
due to the increased complexity. These designs have to adhere to more and more
degrees of freedom as methods refine and/or other methods are used. Examples for
this are physical domains like aerodynamic (i.e. drag), fluid dynamics (i.e.
throughput of liquid) --- where the complexity increases with the temporal and
spatial resolution of the simulation --- or known hard algorithmic problems in
informatics (i.e. layouting of circuit boards or stacking of 3D--objects).
Moreover these are typically not static environments but requirements shift over
time or from case to case.
due to the increased complexity resulting from more and more degrees of freedom
as methods refine and/or other methods are used. Examples for this are physical
domains like aerodynamic (i.e. drag), fluid dynamics (i.e. throughput of liquid)
--- where the complexity increases with the temporal and spatial resolution of
the simulation --- or known hard algorithmic problems in informatics (i.e.
layouting of circuit boards or stacking of 3D--objects). Moreover these are
typically not static environments but requirements shift over time or from case
to case.
Evolutionary algorithms cope especially well with these problem domains while
addressing all the issues at hand\cite{minai2006complex}. One of the main
@ -79,7 +79,8 @@ As we transfer the results of Richter et al.\cite{anrichterEvol} from using
used, namely *regularity*, *variability*, and *improvement potential*. We
introduce these term in detail in Chapter \ref{sec:intro:rvi}. In the original
publication the author could show a correlation between these
evolvability--criteria with the quality and potential of such optimization.
evolvability--criteria with the quality and convergence speed of such
optimization.
We will replicate the same setup on the same objects but use \acf{FFD} instead of
\acf{RBF} to create a local deformation near the control points and evaluate if