masterarbeit/arbeit/ma.md

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fontsize: 12pt
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\chapter*{How to read this Thesis}

As a guide through the nomenclature used in the formulas we prepend this
chapter.

Unless otherwise noted the following holds:

- lowercase letters $x,y,z$  
  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.
- other lowercase letters  
  refer to other scalar (real) variables.
- lowercase **bold** letters (e.g. $\vec{x},\vec{y}$)  
  refer to 3D coordinates
- uppercase **BOLD** letters (e.g. $\vec{D}, \vec{M}$)  
  refer to Matrices

# Introduction

\improvement[inline]{Mehr Bilder}

Many modern industrial design processes require advanced optimization methods
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
concerns in these algorithms is the formulation of the problems in terms of a
*genome* and  *fitness--function*. While one can typically use an arbitrary
cost--function for the *fitness--functions* (i.e. amount of drag, amount of space,
etc.), the translation of the problem--domain into a simple parametric
representation (the *genome*) can be challenging.

This translation is often necessary as the target of the optimization may have
too many degrees of freedom. In the example of an aerodynamic simulation of drag
onto an object, those objects--designs tend to have a high number of vertices to
adhere to various requirements (visual, practical, physical, etc.). A simpler
representation of the same object in only a few parameters that manipulate the
whole in a sensible matter are desirable, as this often decreases the
computation time significantly.

Additionally one can exploit the fact, that drag in this case is especially
sensitive to non--smooth surfaces, so that a smooth local manipulation of the
surface as a whole is more advantageous than merely random manipulation of the
vertices.

The quality of such a low-dimensional representation in biological evolution is
strongly tied to the notion of *evolvability*\cite{wagner1996complex}, as the
parametrization of the problem has serious implications on the convergence speed
and the quality of the solution\cite{Rothlauf2006}.
However, there is no consensus on how *evolvability* is defined and the meaning
varies from context to context\cite{richter2015evolvability}, so there is need
for some criteria we can measure, so that we are able to compare different
representations to learn and improve upon these.

One example of such a general representation of an object is to generate random
points and represent vertices of an object as distances to these points --- for
example via \acf{RBF}. If one (or the algorithm) would move such a point the
object will get deformed locally (due to the \ac{RBF}). As this results in a
simple mapping from the parameter-space onto the object one can try out
different representations of the same object and evaluate the *evolvability*.
This is exactly what Richter et al.\cite{anrichterEvol} have done.

As we transfer the results of Richter et al.\cite{anrichterEvol} from using
\acf{RBF} as a representation to manipulate geometric objects to the use of
\acf{FFD} we will use the same definition for evolvability the original author
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 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
the evolution--criteria still work as a predictor for *evolvability* of the
representation given the different deformation scheme, as suspected in
\cite{anrichterEvol}.

First we introduce different topics in isolation in Chapter \ref{sec:back}. We
take an abstract look at the definition of \ac{FFD} for a one--dimensional line
(in \ref{sec:back:ffd}) and discuss why this is a sensible deformation function
(in \ref{sec:back:ffdgood}).
Then we establish some background--knowledge of evolutionary algorithms (in
\ref{sec:back:evo}) and why this is useful in our domain (in
\ref{sec:back:evogood}).
In a third step we take a look at the definition of the different evolvability
criteria established in \cite{anrichterEvol}.

In Chapter \ref{sec:impl} we take a look at our implementation of \ac{FFD} and
the adaptation for 3D--meshes that were used.

Next, in Chapter \ref{sec:eval}, we describe the different scenarios we use to
evaluate the different evolvability--criteria incorporating all aspects
introduced in Chapter \ref{sec:back}. Following that, we evaluate the results in
Chapter \ref{sec:res} with further on discussion in Chapter \ref{sec:dis}.


# Background
\label{sec:back}

## What is \acf{FFD}?
\label{sec:back:ffd}

First of all we have to establish how a \ac{FFD} works and why this is a good
tool for deforming geometric objects (esp. meshes in our case) in the first
place. For simplicity we only summarize the 1D--case from
\cite{spitzmuller1996bezier} here and go into the extension to the 3D case in
chapter \ref{3dffd}.

The main idea of \ac{FFD} is to create a function $s : [0,1[^d \mapsto
\mathbb{R}^d$ that spans a certain part of a vector--space and is only linearly
parametrized by some special control points $p_i$ and an constant
attribution--function $a_i(u)$, so
$$
s(u) = \sum_i a_i(u) p_i
$$
can be thought of a representation of the inside of the convex hull generated by
the control points where each point can be accessed by the right $u \in [0,1[$.

\begin{figure}[!ht]
\begin{center}
\includegraphics[width=0.7\textwidth]{img/B-Splines.png}
\end{center}
\caption[Example of B-Splines]{Example of a parametrization of a line with
corresponding deformation to generate a deformed objet}
\label{fig:bspline}
\end{figure}

In the example in figure \ref{fig:bspline}, the control--points are indicated as
red dots and the color-gradient should hint at the $u$--values ranging from
$0$ to $1$.

We now define a \acf{FFD} by the following:  
Given an arbitrary number of points $p_i$ alongside a line, we map a scalar
value $\tau_i \in [0,1[$ to each point with $\tau_i < \tau_{i+1} \forall i$
according to the position of $p_i$ on said line.
Additionally, given a degree of the target polynomial $d$ we define the curve
$N_{i,d,\tau_i}(u)$ as follows:

\begin{equation} \label{eqn:ffd1d1}
N_{i,0,\tau}(u) = \begin{cases} 1, & u \in [\tau_i, \tau_{i+1}[ \\ 0, & \mbox{otherwise} \end{cases}
\end{equation}
and
\begin{equation} \label{eqn:ffd1d2}
N_{i,d,\tau}(u) = \frac{u-\tau_i}{\tau_{i+d}} N_{i,d-1,\tau}(u) + \frac{\tau_{i+d+1} - u}{\tau_{i+d+1}-\tau_{i+1}} N_{i+1,d-1,\tau}(u)
\end{equation}

If we now multiply every $p_i$ with the corresponding $N_{i,d,\tau_i}(u)$ we get
the contribution of each point $p_i$ to the final curve--point parameterized only
by $u \in [0,1[$.  As can be seen from \eqref{eqn:ffd1d2} we only access points
$[p_i..p_{i+d}]$ for any given $i$^[one more for each recursive step.], which gives
us, in combination with choosing $p_i$ and $\tau_i$ in order, only a local
interference of $d+1$ points.

We can even derive this equation straightforward for an arbitrary
$N$^[*Warning:* in the case of $d=1$ the recursion--formula yields a $0$
denominator, but $N$ is also $0$. The right solution for this case is a
derivative of $0$]:

$$\frac{\partial}{\partial u} N_{i,d,r}(u) = \frac{d}{\tau_{i+d} - \tau_i} N_{i,d-1,\tau}(u) - \frac{d}{\tau_{i+d+1} - \tau_{i+1}} N_{i+1,d-1,\tau}(u)$$

For a B--Spline
$$s(u) = \sum_{i} N_{i,d,\tau_i}(u) p_i$$
these derivations yield $\frac{\partial^d}{\partial u} s(u) = 0$.

Another interesting property of these recursive polynomials is that they are
continuous (given $d \ge 1$) as every $p_i$ gets blended in between $\tau_i$ and
$\tau_{i+d}$ and out between $\tau_{i+1}$, and $\tau_{i+d+1}$ as can bee seen from the two coefficients
in every step of the recursion.

This means that all changes are only a local linear combination between the
control--point $p_i$ to $p_{i+d+1}$ and consequently this yields to the
convex--hull--property of B-Splines --- meaning, that no matter how we choose
our coefficients, the resulting points all have to lie inside convex--hull of
the control--points.

For a given point $v_i$ we can then calculate the contributions
$n_{i,j}~:=~N_{j,d,\tau}$ of each control point $p_j$ to get the
projection from the control--point--space into the object--space:
$$
v_i = \sum_j n_{i,j} \cdot p_j = \vec{n}_i^{T} \vec{p}
$$
or written for all points at the same time:
$$
\vec{v} = \vec{N} \vec{p}
$$
where $\vec{N}$ is the $n \times m$ transformation--matrix (later on called
**deformation matrix**) for $n$ object--space--points and $m$ control--points.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=\textwidth]{img/unity.png}
\end{center}
\caption[B--spline--basis--function as partition of unity]{From \cite[Figure 2.13]{brunet2010contributions}:\newline
\glqq Some interesting properties of the B--splines. On the natural definition domain
of the B--spline ($[k_0,k_4]$ on this figure), the B--spline basis functions sum
up to one (partition of unity). In this example, we use B--splines of degree 2.
The horizontal segment below the abscissa axis represents the domain of
influence of the B--splines basis function, i.e. the interval on which they are
not null. At a given point, there are at most $ d+1$ non-zero B--spline basis
functions (compact support).\grqq \newline
Note, that Brunet starts his index at $-d$ opposed to our definition, where we
start at $0$.}
\label{fig:partition_unity}
\end{figure}

Furthermore B--splines--basis--functions form a partition of unity for all, but
the first and last $d$ control-points\cite{brunet2010contributions}. Therefore
we later on use the border-points $d+1$ times, such that $\sum_j n_{i,j} p_j = p_i$
for these points.

The locality of the influence of each control--point and the partition of unity
was beautifully pictured by Brunet, which we included here as figure
\ref{fig:partition_unity}.

### Why is \ac{FFD} a good deformation function?
\label{sec:back:ffdgood}

The usage of \ac{FFD} as a tool for manipulating follows directly from the
properties of the polynomials and the correspondence to the control points.
Having only a few control points gives the user a nicer high--level--interface, as
she only needs to move these points and the model follows in an intuitive
manner. The deformation is smooth as the underlying polygon is smooth as well
and affects as many vertices of the model as needed. Moreover the changes are
always local so one risks not any change that a user cannot immediately see.

But there are also disadvantages of this approach. The user loses the ability to
directly influence vertices and even seemingly simple tasks as creating a
plateau can be difficult to
achieve\cite[chapter~3.2]{hsu1991dmffd}\cite{hsu1992direct}.

This disadvantages led to the formulation of
\acf{DM--FFD}\cite[chapter~3.3]{hsu1991dmffd} in which the user directly
interacts with the surface--mesh.  All interactions will be applied
proportionally to the control--points that make up the parametrization of the
interaction--point itself yielding a smooth deformation of the surface *at* the
surface without seemingly arbitrary scattered control--points.  Moreover this
increases the efficiency of an evolutionary optimization\cite{Menzel2006}, which
we will use later on.

\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/hsu_fig7.png}
\caption{Figure 7 from \cite{hsu1991dmffd}.}
\label{fig:hsu_fig7}
\end{figure}

But this approach also has downsides as can be seen in figure
\ref{fig:hsu_fig7}, as the tessellation of the invisible grid has a major impact
on the deformation itself.

All in all \ac{FFD} and \ac{DM--FFD} are still good ways to deform a high--polygon
mesh albeit the downsides.

## What is evolutionary optimization?
\label{sec:back:evo}

In this thesis we are using an evolutionary optimization strategy to solve the
problem of finding the best parameters for our deformation. This approach,
however, is very generic and we introduce it here in a broader sense.

\begin{algorithm}
\caption{An outline of evolutionary algorithms}
\label{alg:evo}
\begin{algorithmic}
\STATE t := 0;
\STATE initialize $P(0) := \{\vec{a}_1(0),\dots,\vec{a}_\mu(0)\} \in I^\mu$;
\STATE evaluate $F(0) : \{\Phi(x) | x \in P(0)\}$;
\WHILE{$c(F(t)) \neq$ \TRUE}
    \STATE recombine: $P’(t) := r(P(t))$;
    \STATE mutate: $P''(t) := m(P’(t))$;
    \STATE evaluate $F''(t) : \{\Phi(x) | x \in P''(t)\}$
    \STATE select: $P(t + 1) := s(P''(t) \cup Q,\Phi)$;
    \STATE t := t + 1;
\ENDWHILE
\end{algorithmic}
\end{algorithm}

The general shape of an evolutionary algorithm (adapted from
\cite{back1993overview}) is outlined in Algorithm \ref{alg:evo}. Here, $P(t)$ 
denotes the population of parameters in step $t$ of the algorithm. The
population contains $\mu$ individuals $a_i$ from the possible individual--set
$I$ that fit the shape of the parameters we are looking for. Typically these are
initialized by a random guess or just zero. Further on we need a so--called
*fitness--function* $\Phi : I \mapsto M$ that can take each parameter to a measurable
space $M$ (usually $M = \mathbb{R}$) along a convergence--function $c : I \mapsto \mathbb{B}$
that terminates the optimization.

Biologically speaking the set $I$ corresponds to the set of possible *Genotypes*
while $M$ represents the possible observable *Phenotypes*.

The main algorithm just repeats the following steps:

- **Recombine** with a recombination--function $r : I^{\mu} \mapsto I^{\lambda}$ to
  generate $\lambda$ new individuals based on the characteristics of the $\mu$
  parents.  
  This makes sure that the next guess is close to the old guess.
- **Mutate** with a mutation--function $m : I^{\lambda} \mapsto I^{\lambda}$ to
  introduce new effects that cannot be produced by mere recombination of the
  parents.  
  Typically this just adds minor defects to individual members of the population
  like adding a random gaussian noise or amplifying/dampening random parts.
- **Selection** takes a selection--function $s : (I^\lambda \cup I^{\mu + \lambda},\Phi) \mapsto I^\mu$ that
  selects from the previously generated $I^\lambda$ children and optionally also
  the parents (denoted by the set $Q$ in the algorithm) using the
  fitness--function $\Phi$. The result of this operation is the next Population
  of $\mu$ individuals.

All these functions can (and mostly do) have a lot of hidden parameters that
can be changed over time. One can for example start off with a high
mutation--rate that cools off over time (i.e. by lowering the variance of a
gaussian noise).

## Advantages of evolutionary algorithms
\label{sec:back:evogood}

The main advantage of evolutionary algorithms is the ability to find optima of
general functions just with the help of a given fitness--function. With this
most problems of simple gradient--based procedures, which often target the same
error--function which measures the fitness, as an evolutionary algorithm, but can
easily get stuck in local optima.

Components and techniques for evolutionary algorithms are specifically known to
help with different problems arising in the domain of
optimization\cite{weise2012evolutionary}. An overview of the typical problems
are shown in figure \ref{fig:probhard}.

\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/weise_fig3.png}
\caption{Fig.~3. taken from \cite{weise2012evolutionary}}
\label{fig:probhard}
\end{figure}

Most of the advantages stem from the fact that a gradient--based procedure has
only one point of observation from where it evaluates the next steps, whereas an
evolutionary strategy starts with a population of guessed solutions. Because an
evolutionary strategy modifies the solution randomly, keeps the best solutions
and purges the worst, it can also target multiple different hypothesis at the
same time where the local optima die out in the face of other, better
candidates.

If an analytic best solution exists and is easily computable (i.e. because the
error--function is convex) an evolutionary algorithm is not the right choice.
Although both converge to the same solution, the analytic one is usually faster.

But in reality many problems have no analytic solution, because the problem is
either not convex or there are so many parameters that an analytic solution
(mostly meaning the equivalence to an exhaustive search) is computationally not
feasible. Here evolutionary optimization has one more advantage as you can at
least get suboptimal solutions fast, which then refine over time.

## Criteria for the evolvability of linear deformations
\label{sec:intro:rvi}

As we have established in chapter \ref{sec:back:ffd}, we can describe a
deformation by the formula
$$
V = UP
$$
where $V$ is a $n \times d$ matrix of vertices, $U$ are the (during
parametrization) calculated deformation--coefficients and $P$ is a $m \times d$ matrix
of control--points that we interact with during deformation.

We can also think of the deformation in terms of differences from the original
coordinates
$$
\Delta V = U \cdot \Delta P
$$
which is isomorphic to the former due to the linear correlation in the
deformation. One can see in this way, that the way the deformation behaves lies
solely in the entries of $U$, which is why the three criteria focus on this.

### Variability

In \cite{anrichterEvol} *variability* is defined as
$$V(\vec{U}) := \frac{\textrm{rank}(\vec{U})}{n},$$
whereby $\vec{U}$ is the $n \times m$ deformation--Matrix \unsure{Nicht $(n\cdot d) \times m$? Wegen $u,v,w$?} used to map the $m$
control points onto the $n$ vertices.

Given $n = m$, an identical number of control--points and vertices, this
quotient will be $=1$ if all control points are independent of each other and
the solution is to trivially move every control--point onto a target--point.

In praxis the value of $V(\vec{U})$ is typically $\ll 1$, because as
there are only few control--points for many vertices, so $m \ll n$.

### Regularity

*Regularity* is defined\cite{anrichterEvol} as
$$R(\vec{U}) := \frac{1}{\kappa(\vec{U})} = \frac{\sigma_{min}}{\sigma_{max}}$$
where $\sigma_{min}$ and $\sigma_{max}$ are the smallest and greatest right singular
value of the deformation--matrix $\vec{U}$.

As we deform the given Object only based on the parameters as $\vec{p} \mapsto
f(\vec{x} + \vec{U}\vec{p})$ this makes sure that $\|\vec{Up}\| \propto
\|\vec{p}\|$ when $\kappa(\vec{U}) \approx 1$. The inversion of $\kappa(\vec{U})$
is only performed to map the criterion--range to $[0..1]$, whereas $1$ is the
optimal value and $0$ is the worst value.

On the one hand this criterion should be characteristic for numeric
stability\cite[chapter 2.7]{golub2012matrix} and on the other hand for the
convergence speed of evolutionary algorithms\cite{anrichterEvol} as it is tied to 
the notion of locality\cite{weise2012evolutionary,thorhauer2014locality}.

### Improvement Potential

In contrast to the general nature of *variability* and *regularity*, which are
agnostic of the fitness--function at hand the third criterion should reflect a
notion of potential.

As during optimization some kind of gradient $g$ is available to suggest a
direction worth pursuing we use this to guess how much change can be achieved in
the given direction.

The definition for an *improvement potential* $P$ is\cite{anrichterEvol}:
$$
P(\vec{U}) := 1 - \|(\vec{1} - \vec{UU}^+)\vec(G)\|^2_F
$$
given some approximate $n \times d$ fitness--gradient $\vec{G}$, normalized to
$\|\vec{G}\|_F = 1$, whereby $\|\cdot\|_F$ denotes the Frobenius--Norm.

# Implementation of \acf{FFD}
\label{sec:impl}

The general formulation of B--Splines has two free parameters $d$ and $\tau$
which must be chosen beforehand.

As we usually work with regular grids in our \ac{FFD} we define $\tau$
statically as $\tau_i = \nicefrac{i}{n}$ whereby $n$ is the number of
control--points in that direction.

$d$ defines the *degree* of the B--Spline--Function (the number of times this
function is differentiable) and for our purposes we fix $d$ to $3$, but give the
formulas for the general case so it can be adapted quite freely.


## Adaption of \ac{FFD}
\label{sec:ffd:adapt}

As we have established in Chapter \ref{sec:back:ffd} we can define an
\ac{FFD}--displacement as
\begin{equation}
\Delta_x(u) = \sum_i N_{i,d,\tau_i}(u) \Delta_x c_i
\end{equation}

Note that we only sum up the $\Delta$--displacements in the control points $c_i$ to get
the change in position of the point we are interested in.

In this way every deformed vertex is defined by
$$
\textrm{Deform}(v_x) = v_x + \Delta_x(u)
$$
with $u \in [0..1[$ being the variable that connects the high--detailed
vertex--mesh to the low--detailed control--grid. To actually calculate the new
position of the vertex we first have to calculate the $u$--value for each
vertex. This is achieved by finding out the parametrization of $v$ in terms of
$c_i$
$$
v_x \overset{!}{=} \sum_i N_{i,d,\tau_i}(u) c_i
$$
so we can minimize the error between those two:
$$
\underset{u}{\argmin}\,Err(u,v_x) = \underset{u}{\argmin}\,2 \cdot \|v_x - \sum_i N_{i,d,\tau_i}(u) c_i\|^2_2
$$
As this error--term is quadratic we just derive by $u$ yielding
$$
\begin{array}{rl}
\frac{\partial}{\partial u} & v_x - \sum_i N_{i,d,\tau_i}(u) c_i \\
= & - \sum_i \left( \frac{d}{\tau_{i+d} - \tau_i} N_{i,d-1,\tau}(u) - \frac{d}{\tau_{i+d+1} - \tau_{i+1}} N_{i+1,d-1,\tau}(u) \right) c_i
\end{array}
$$
and do a gradient--descend to approximate the value of $u$ up to an $\epsilon$ of $0.0001$.

For this we use the Gauss--Newton algorithm\cite{gaussNewton} as the solution to
this problem may not be deterministic, because we usually have way more vertices
than control points ($\#v~\gg~\#c$).

## Adaption of \ac{FFD} for a 3D--Mesh
\label{3dffd}

This is a straightforward extension of the 1D--method presented in the last
chapter. But this time things get a bit more complicated. As we have a
3--dimensional grid we may have a different amount of control--points in each
direction.

Given $n,m,o$ control points in $x,y,z$--direction each Point on the curve is
defined by
$$V(u,v,w) = \sum_i \sum_j \sum_k N_{i,d,\tau_i}(u) N_{j,d,\tau_j}(v) N_{k,d,\tau_k}(w) \cdot C_{ijk}.$$

In this case we have three different B--Splines (one for each dimension) and also
3 variables $u,v,w$ for each vertex we want to approximate.

Given a target vertex $\vec{p}^*$ and an initial guess $\vec{p}=V(u,v,w)$
we define the error--function for the gradient--descent as:

$$Err(u,v,w,\vec{p}^{*}) = \vec{p}^{*} - V(u,v,w)$$

And the partial version for just one direction as

$$Err_x(u,v,w,\vec{p}^{*}) = p^{*}_x - \sum_i \sum_j \sum_k N_{i,d,\tau_i}(u) N_{j,d,\tau_j}(v) N_{k,d,\tau_k}(w) \cdot {c_{ijk}}_x $$

To solve this we derive partially, like before:

$$
\begin{array}{rl}
    \displaystyle \frac{\partial Err_x}{\partial u} & p^{*}_x - \displaystyle \sum_i \sum_j \sum_k N_{i,d,\tau_i}(u) N_{j,d,\tau_j}(v) N_{k,d,\tau_k}(w) \cdot {c_{ijk}}_x \\
  = & \displaystyle - \sum_i \sum_j \sum_k N'_{i,d,\tau_i}(u) N_{j,d,\tau_j}(v) N_{k,d,\tau_k}(w) \cdot {c_{ijk}}_x
\end{array}
$$

The other partial derivatives follow the same pattern yielding the Jacobian:

$$
J(Err(u,v,w)) = 
\left(
\begin{array}{ccc}
\frac{\partial Err_x}{\partial u} & \frac{\partial Err_x}{\partial v} & \frac{\partial Err_x}{\partial w} \\
\frac{\partial Err_y}{\partial u} & \frac{\partial Err_y}{\partial v} & \frac{\partial Err_y}{\partial w} \\
\frac{\partial Err_z}{\partial u} & \frac{\partial Err_z}{\partial v} & \frac{\partial Err_z}{\partial w}
\end{array}
\right)
$$
$$
\scriptsize
=
\left(
\begin{array}{ccc}
- \displaystyle \sum_{i,j,k} N'_{i}(u) N_{j}(v) N_{k}(w) \cdot {c_{ijk}}_x &- \displaystyle \sum_{i,j,k} N_{i}(u) N'_{j}(v) N_{k}(w) \cdot {c_{ijk}}_x & - \displaystyle \sum_{i,j,k} N_{i}(u) N_{j}(v) N'_{k}(w) \cdot {c_{ijk}}_x \\
- \displaystyle \sum_{i,j,k} N'_{i}(u) N_{j}(v) N_{k}(w) \cdot {c_{ijk}}_y &- \displaystyle \sum_{i,j,k} N_{i}(u) N'_{j}(v) N_{k}(w) \cdot {c_{ijk}}_y & - \displaystyle \sum_{i,j,k} N_{i}(u) N_{j}(v) N'_{k}(w) \cdot {c_{ijk}}_y \\
- \displaystyle \sum_{i,j,k} N'_{i}(u) N_{j}(v) N_{k}(w) \cdot {c_{ijk}}_z &- \displaystyle \sum_{i,j,k} N_{i}(u) N'_{j}(v) N_{k}(w) \cdot {c_{ijk}}_z & - \displaystyle \sum_{i,j,k} N_{i}(u) N_{j}(v) N'_{k}(w) \cdot {c_{ijk}}_z
\end{array}
\right)
$$

With the Gauss--Newton algorithm we iterate via the formula
$$J(Err(u,v,w)) \cdot \Delta \left( \begin{array}{c} u \\ v \\ w \end{array} \right) = -Err(u,v,w)$$
and use Cramers rule for inverting the small Jacobian and solving this system of
linear equations.

## Deformation Grid

As mentioned in chapter \ref{sec:back:evo}, the way of choosing the
representation to map the general problem (mesh--fitting/optimization in our
case) into a parameter-space it very important for the quality and runtime of
evolutionary algorithms\cite{Rothlauf2006}.

Because our control--points are arranged in a grid, we can accurately represent
each vertex--point inside the grids volume with proper B--Spline--coefficients
between $[0,1[$ and --- as a consequence --- we have to embed our object into it
(or create constant "dummy"-points outside).

The great advantage of B--Splines is the locality, direct impact of each
control point without having a $1:1$--correlation, and a smooth deformation.
While the advantages are great, the issues arise from the problem to decide
where to place the control--points and how many.

One would normally think, that the more control--points you add, the better the
result will be, but this is not the case for our B--Splines. Given any point $p$
only the $2 \cdot (d-1)$ control--points contribute to the parametrization of
that point^[Normally these are $d-1$ to each side, but at the boundaries the
number gets increased to the inside to meet the required smoothness].
This means, that a high resolution can have many control-points that are not
contributing to any point on the surface and are thus completely irrelevant to
the solution.

\begin{figure}[!ht]
\begin{center}
\includegraphics{img/enoughCP.png}
\end{center}
\caption[Example of a high resolution control--grid]{A high resolution
($10 \times 10$) of control--points over a circle. Yellow/green points
contribute to the parametrization, red points don't.\newline
An Example--point (blue) is solely determined by the position of the green
control--points.}
\label{fig:enoughCP}
\end{figure}


We illustrate this phenomenon in figure \ref{fig:enoughCP}, where the four red 
central points are not relevant for the parametrization of the circle.

\unsure[inline]{erwähnen, dass man aus $\vec{D}$ einfach die Null--Spalten
entfernen kann?}

For our tests we chose different uniformly sized grids and added noise
onto each control-point^[For the special case of the outer layer we only applied
noise away from the object, so the object is still confined in the convex hull
of the control--points.] to simulate different starting-conditions.

\unsure[inline]{verweis auf DM--FFD?}

# Scenarios for testing evolvability criteria using \acf{FFD}
\label{sec:eval}

In our experiments we use the same two testing--scenarios, that were also used
by \cite{anrichterEvol}. The first scenario deforms a plane into a shape
originally defined in \cite{giannelli2012thb}, where we setup control-points in
a 2--dimensional manner merely deform in the height--coordinate to get the
resulting shape.

In the second scenario we increase the degrees of freedom significantly by using
a 3--dimensional control--grid to deform a sphere into a face. So each control
point has three degrees of freedom in contrast to first scenario.

## Test Scenario: 1D Function Approximation

In this scenario we used the shape defined by Giannelli et al.\cite{giannelli2012thb},
which is also used by Richter et al.\cite{anrichterEvol} using the same
discretization to $150 \times 150$ points for a total of $n = 22\,500$ vertices. The
shape is given by the following definition
$$
t(x,y) =
\begin{cases}
0.5 \cos(4\pi \cdot q^{0.5}) + 0.5 & q(x,y) < \frac{1}{16},\\
2(y-x) & 0 < y-x < 0.5,\\
1 & 0.5 < y - x
\end{cases}
$$
with $(x,y) \in [0,2] \times [0,1]$ and $q(x,y)=(x-1.5)^2 + (y-0.5)^2$, which we have
visualized in figure \ref{fig:1dtarget}.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=0.7\textwidth]{img/1dtarget.png}
\end{center}
\caption[The 1D--target--shape]{The target--shape for our 1--dimensional optimization--scenario
including a wireframe--overlay of the vertices.}
\label{fig:1dtarget}
\end{figure}

As the starting-plane we used the same shape, but set all
$z$--coordinates to $0$, yielding a flat plane, which is partially already
correct.

Regarding the *fitness--function* $f(\vec{p})$, we use the very simple approach
of calculating the squared distances for each corresponding vertex
$$
\textrm{f(\vec{p})} = \sum_{i=1}^{n} \|(\vec{Up})_i - t_i\|_2^2 = \|\vec{Up} - \vec{t}\|^2 \rightarrow \min
$$
where $t_i$ are the respective target--vertices to the parametrized
source--vertices^[The parametrization is encoded in $\vec{U}$ and the initial
position of the control points. See \ref{sec:ffd:adapt}] with the current
deformation--parameters $\vec{p} = (p_1,\dots, p_m)$. We can do this
one--to--one--correspondence because we have exactly the same number of
source and target-vertices do to our setup of just flattening the object.

This formula is also the least--squares approximation error for which we
can compute the analytic solution $\vec{p^{*}} = \vec{U^+}\vec{t}$, yielding us
the correct gradient in which the evolutionary optimizer should move.

## Procedure: 1D Function Approximation
\label{sec:proc:1d}

For our setup we first compute the coefficients of the deformation--matrix and
use then the formulas for *variability* and *regularity* to get our predictions.
Afterwards we solve the problem analytically to get the (normalized) correct
gradient that we use as guess for the *improvement potential*. To check we also
consider a distorted gradient $\vec{g}_{\textrm{d}}$
$$
\vec{g}_{\textrm{d}} = \frac{\vec{g}_{\textrm{c}} + \mathbb{1}}{\|\vec{g}_{\textrm{c}} + \mathbb{1}\|}
$$
where $\mathbb{1}$ is the vector consisting of $1$ in every dimension and
$\vec{g}_\textrm{c} = \vec{p^{*}}$ the calculated correct gradient.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=\textwidth]{img/example1d_grid.png}
\end{center}
\caption[Example of a 1D--grid]{\newline Left: A regular $7 \times 4$--grid\newline Right: The same grid after a
random distortion to generate a testcase.}
\label{fig:example1d_grid}
\end{figure}

We then set up a regular 2--dimensional grid around the object with the desired
grid resolutions. To generate a testcase we then move the grid--vertices
randomly inside the x--y--plane. As self-intersecting grids get tricky to solve
with our implemented newtons--method we avoid the generation of such
self--intersecting grids for our testcases.

This is a reasonable thing to do, as self-intersecting grids violate our desired
property of locality, as the then farther away control--point has more influence
over some vertices as the next-closer.

To achieve that we select a uniform distributed number $r \in [-0.25,0.25]$ per
dimension and shrink the distance to the neighbours (the smaller neighbour for
$r < 0$, the larger for $r > 0$) by the factor $r$^[Note: On the Edges this
displacement is only applied outwards by flipping the sign of $r$, if
appropriate.].
\improvement[inline]{update!! gaussian, not uniform!!}

An Example of such a testcase can be seen for a $7 \times 4$--grid in figure
\ref{fig:example1d_grid}.

## Test Scenario: 3D Function Approximation

Opposed to the 1--dimensional scenario before, the 3--dimensional scenario is
much more complex --- not only because we have more degrees of freedom on each
control point, but also because the *fitness--function* we will use has no known
analytic solution and multiple local minima.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=0.7\textwidth]{img/3dtarget.png}
\end{center}
\caption[3D source and target meshes]{\newline
Left: The sphere we start from with 10\,807 vertices\newline
Right: The face we want to deform the sphere into with 12\,024 vertices.}
\label{fig:3dtarget}
\end{figure}

First of all we introduce the set up: We have given a triangulated model of a
sphere consisting of 10\,807 vertices, that we want to deform into a
the target--model of a face with a total of 12\,024 vertices. Both of
these Models can be seen in figure \ref{fig:3dtarget}.

Opposed to the 1D--case we cannot map the source and target--vertices in a
one--to--one--correspondence, which we especially need for the approximation of
the fitting--error. Hence we state that the error of one vertex is the distance
to the closest vertex of the other model.

We therefore define the *fitness--function* to be:
$$
f(\vec{P}) = \frac{1}{n} \underbrace{\sum_{i=1}^n \|\vec{c_T(s_i)} -
\vec{s_i}\|_2^2}_{\textrm{source-to-target--distance}}
+ \frac{1}{m} \underbrace{\sum_{i=1}^m \|\vec{c_S(t_i)} -
\vec{t_i}\|_2^2}_{\textrm{target-to-source--distance}}
+ \lambda \cdot \textrm{regularization}(\vec{P})
$$
where $\vec{c_T(s_i)}$ denotes the target--vertex that is corresponding to the
source--vertex $\vec{s_i}$ and $\vec{c_S(t_i)}$ denotes the source--vertex that
corresponds to the target--vertex $\vec{t_i}$. Note that the target--vertices
are given and fixed by the target--model of the face we want to deform into,
whereas the source--vertices vary depending on the chosen parameters $\vec{P}$,
as those get calculated by the previously introduces formula $\vec{S} = \vec{UP}$
with $\vec{S}$ being the $n \times 3$--matrix of source--vertices, $\vec{U}$ the
$n \times m$--matrix of calculated coefficients for the \ac{FFD} --- analog to
the 1D case --- and finally $\vec{P}$ being the $m \times 3$--matrix of the
control--grid defining the whole deformation.

As regularization-term we add a weighted Laplacian of the deformation that has
been used before by Aschenbach et al.\cite[Section 3.2]{aschenbach2015} on
similar models and was shown to lead to a more precise fit. The Laplacian
$$
\textrm{regularization}(\vec{P}) = \frac{1}{\sum_i A_i} \sum_{i=1}^n A_i \cdot \left( \sum_{\vec{s_j} \in \mathcal{N}(\vec{s_i})} w_j \cdot \|\Delta \vec{s_j} - \Delta \vec{\overline{s}_j}\|^2 \right)
$$
is determined by the cotangent weighted displacement $w_j$ of the to $s_i$
connected vertices $\mathcal{N}(s_i)$ and $A_i$ is the Voronoi--area of the corresponding vertex
$\vec{s_i}$. We leave out the $\vec{R}_i$--term from the original paper as our
deformation is merely linear.

This regularization--weight gives us a measure of stiffness for the material
that we will influence via the $\lambda$--coefficient to start out with a stiff
material that will get more flexible per iteration.
\unsure[inline]{Andreas: hast du nen cite, wo gezeigt ist, dass das so sinnvoll ist?}

## Procedure: 3D Function Approximation

Initially we set up the correspondences $\vec{c_T(\dots)}$ and $\vec{c_S(\dots)}$ to be
the respectively closest vertices of the other model. We then calculate the
analytical solution given these correspondences via $\vec{P^{*}} = \vec{U^+}\vec{T}$,
and also use the first solution as guessed gradient for the calculation of the
*improvement--potential*, as the optimal solution is not known.
We then let the evolutionary algorithm run up within $1.05$ times the error of 
this solution and afterwards recalculate the correspondences $\vec{c_T(\dots)}$
and $\vec{c_S(\dots)}$.
For the next step we then halve the regularization--impact $\lambda$ and
calculate the next incremental solution $\vec{P^{*}} = \vec{U^+}\vec{T}$ with
the updated correspondences to get our next target--error.
We repeat this process as long as the target--error keeps decreasing.

\begin{figure}[ht]
\begin{center}
\includegraphics[width=\textwidth]{img/example3d_grid.png}
\end{center}
\caption[Example of a 3D--grid]{\newline Left: The 3D--setup with a $4\times
4\times 4$--grid.\newline Right: The same grid after added noise to the
control--points.}
\label{fig:setup3d}
\end{figure}

The grid we use for our experiments is just very coarse due to computational
limitations. We are not interested in a good reconstruction, but an estimate if
the mentioned evolvability criteria are good.

In figure \ref{fig:setup3d} we show an example setup of the scene with a
$4\times 4\times 4$--grid. Identical to the 1--dimensional scenario before, we create a
regular grid and move the control-points uniformly random between their
neighbours, but in three instead of two dimensions^[Again, we flip the signs for
the edges, if necessary to have the object still in the convex hull.].

As is clearly visible from figure \ref{fig:3dtarget}, the target--model has many
vertices in the facial area, at the ears and in the neck--region. Therefore we
chose to increase the grid-resolutions for our tests in two different dimensions
and see how well the criteria predict a suboptimal placement of these
control-points.

# Evaluation of Scenarios
\label{sec:res}

To compare our results to the ones given by Richter et al.\cite{anrichterEvol},
we also use Spearman's rank correlation coefficient. Opposed to other popular
coefficients, like the Pearson correlation coefficient, which measures a linear
relationship between variables, the Spearmans's coefficient assesses \glqq how
well an arbitrary monotonic function can descripbe the relationship between two
variables, without making any assumptions about the frequency distribution of
the variables\grqq\cite{hauke2011comparison}.

As we don't have any prior knowledge if any of the criteria is linear and we are
just interested in a monotonic relation between the criteria and their
predictive power, the Spearman's coefficient seems to fit out scenario best.

For interpretation of these values we follow the same interpretation used in
\cite{anrichterEvol}, based on \cite{weir2015spearman}: The coefficient
intervals $r_S \in [0,0.2[$, $[0.2,0.4[$, $[0.4,0.6[$, $[0.6,0.8[$, and $[0.8,1]$ are
classified as *very weak*, *weak*, *moderate*, *strong* and *very strong*. We
interpret p--values smaller than $0.1$ as *significant* and cut off the
precision of p--values after four decimal digits (thus often having a p--value
of $0$ given for p--values $< 10^{-4}$).

As we are looking for anti--correlation (i.e. our criterion should be maximized
indicating a minimal result in --- for example --- the reconstruction--error)
instead of correlation we flip the sign of the correlation--coefficient for
readability and to have the correlation--coefficients be in the
classification--range given above.

For the evolutionary optimization we employ the CMA--ES (covariance matrix
adaptation evolution strategy) of the shark3.1 library \cite{shark08}, as this
algorithm was used by \cite{anrichterEvol} as well. We leave the parameters at
their sensible defaults as further explained in
\cite[Appendix~A: Table~1]{hansen2016cma}.

## Results of 1D Function Approximation

In the case of our 1D--Optimization--problem, we have the luxury of knowing the
analytical solution to the given problem--set. We use this to experimentally
evaluate the quality criteria we introduced before. As an evolutional
optimization is partially a random process, we use the analytical solution as a
stopping-criteria. We measure the convergence speed as number of iterations the
evolutional algorithm needed to get within $1.05\%$ of the optimal solution.

We used different regular grids that we manipulated as explained in Section
\ref{sec:proc:1d} with a different number of control points. As our grids have
to be the product of two integers, we compared a $5 \times 5$--grid with $25$
control--points to a $4 \times 7$ and $7 \times 4$--grid with $28$
control--points. This was done to measure the impact an \glqq improper\grqq
setup could have and how well this is displayed in the criteria we are
examining.

Additionally we also measured the effect of increasing the total resolution of
the grid by taking a closer look at $5 \times 5$, $7 \times 7$ and $10 \times 10$ grids.

\begin{figure}[ht]
\centering
\includegraphics[width=0.7\textwidth]{img/evolution1d/variability_boxplot.png}
\caption[1D Fitting Errors for various grids]{The squared error for the various
grids we examined.\newline
Note that $7 \times 4$ and $4 \times 7$ have the same number of control--points.}
\label{fig:1dvar}
\end{figure}

### Variability

Variability should characterize the potential for design space exploration and
is defined in terms of the normalized rank of the deformation matrix $\vec{U}$:
$V(\vec{U}) := \frac{\textrm{rank}(\vec{U})}{n}$, whereby $n$ is the number of
vertices.
As all our tested matrices had a constant rank (being $m = x \cdot y$ for a $x \times y$
grid), we have merely plotted the errors in the boxplot in figure
\ref{fig:1dvar}

It is also noticeable, that although the $7 \times 4$ and $4 \times 7$ grids
have a higher variability, they perform not better than the $5 \times 5$ grid.
Also the $7 \times 4$ and $4 \times 7$ grids differ distinctly from each other,
although they have the same number of control--points. This is an indication the
impact a proper or improper grid--setup can have. We do not draw scientific
conclusions from these findings, as more research on non-squared grids seem
necessary.\todo{machen wir die noch? :D}

Leaving the issue of the grid--layout aside we focused on grids having the same
number of prototypes in every dimension. For the $5 \times 5$, $7 \times 7$ and
$10 \times 10$ grids we found a *very strong* correlation ($-r_S = 0.94, p = 0$)
between the variability and the evolutionary error.

### Regularity

\begin{table}[bht]
\centering
\begin{tabular}{c|c|c|c|c}
$5 \times 5$ & $7 \times 4$ & $4 \times 7$ & $7 \times 7$ & $10 \times 10$\\
\hline
$0.28$ ($0.0045$) & \textcolor{red}{$0.21$} ($0.0396$) & \textcolor{red}{$0.1$} ($0.3019$) & \textcolor{red}{$0.01$} ($0.9216$) & \textcolor{red}{$0.01$} ($0.9185$)
\end{tabular}
\caption[Correlation 1D Regularity/Steps]{Spearman's correlation (and p-values)
between regularity and convergence speed for the 1D function approximation
problem.\newline
Not significant entries are marked in red.
}
\label{tab:1dreg}
\end{table}

\begin{figure}[ht]
\centering
\includegraphics[width=\textwidth]{img/evolution1d/55_to_1010_steps.png}
\caption[Improvement potential and regularity vs. steps]{\newline
Left: Improvement potential against steps until convergence\newline
Right: Regularity against steps until convergence\newline
Coloured by their grid--resolution, both with a linear fit over the whole
dataset.}
\label{fig:1dreg}
\end{figure}

Regularity should correspond to the convergence speed (measured in
iteration--steps of the evolutionary algorithm), and is computed as inverse
condition number $\kappa(\vec{U})$ of the deformation--matrix.

As can be seen from table \ref{tab:1dreg}, we could only show a *weak* correlation
in the case of a $5 \times 5$ grid. As we increment the number of
control--points the correlation gets worse until it is completely random in a
single dataset. Taking all presented datasets into account we even get a *strong*
correlation of $- r_S = -0.72, p = 0$, that is opposed to our expectations.

To explain this discrepancy we took a closer look at what caused these high number
of iterations. In figure \ref{fig:1dreg} we also plotted the
improvement-potential against the steps next to the regularity--plot. Our theory
is that the *very strong* correlation ($-r_S = -0.82, p=0$) between
improvement--potential and number of iterations hints that the employed
algorithm simply takes longer to converge on a better solution (as seen in
figure \ref{fig:1dvar} and \ref{fig:1dimp}) offsetting any gain the regularity--measurement could
achieve.

### Improvement Potential

- Alle Spearman 1 und p-value 0.

\begin{figure}[ht]
\centering
\includegraphics[width=0.8\textwidth]{img/evolution1d/55_to_1010_improvement-vs-evo-error.png}
\caption[Correlation 1D Improvement vs. Error]{Improvement potential plotted
against the error yielded by the evolutionary optimization for different
grid--resolutions}
\label{fig:1dimp}
\end{figure}

<!-- ![Improvement potential vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-steps.png) -->
<!--  -->
<!-- ![Improvement potential vs evolutional error](img/evolution1d/20170830-evolution1D_5x5_100Times-all_improvement-vs-evo-error.png) -->
<!--  -->
<!-- ![Regularity vs steps](img/evolution1d/20170830-evolution1D_5x5_100Times-all_regularity-vs-steps.png) -->

## Results of 3D Function Approximation


\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/evolution3d/4x4xX_montage.png}
\caption{Results 3D for 4x4xX}
\end{figure}

\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/evolution3d/Xx4x4_montage.png}
\caption{Results 3D for Xx4x4}
\end{figure}

\begin{figure}[!ht]
\includegraphics[width=\textwidth]{img/evolution3d/YxYxY_montage.png}
\caption{Results 3D for YxYxY for Y $\in [4,5,6]$}
\end{figure}

<!-- ![Improvement potential vs steps](img/evolution3d/20170926_3dFit_both_improvement-vs-steps.png) -->
<!--  -->
<!-- ![Improvement potential vs evolutional -->
<!-- error](img/evolution3d/20170926_3dFit_both_improvement-vs-evo-error.png) -->
<!--  -->
<!-- ![Regularity vs steps](img/evolution3d/20170926_3dFit_both_regularity-vs-steps.png) -->

# Schluss
\label{sec:dis}

- Regularity ist kacke für unser setup. Bessere Vorschläge? EW/EV?

\improvement[inline]{Bibliotheksverzeichnis links anpassen. DOI überschreibt
Direktlinks des Autors.}