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diff --git a/chapters/5_incremental.tex b/chapters/5_incremental.tex
index ff8b793..713648e 100644
--- a/chapters/5_incremental.tex
+++ b/chapters/5_incremental.tex
@@ -566,15 +566,6 @@ We implemented both approaches using the \nanozinc\ prototype, with the results
 The incremental \nanozinc\ translation shows a 12x speedup compared to re-compiling the model from scratch in each iteration.
 For this particular problem, incrementally instructing the target solver (\gls{gecode}) does not lead to a significant reduction in runtime.
 
-\begin{figure}
-	\centering
-	\includegraphics[width=0.5\columnwidth]{assets/img/6_gbac}
-	\caption{\label{fig:6-gbac}A run-time performance comparison between incremental processing (Incr.)
-	and re-evaluation (Redo) of 5 GBAC \minizinc\ instances in the application of \gls{lns} on a 3.4 GHz Quad-Core Intel Core i5 using the Gecode 6.1.2 solver.
-	Each run consisted of 2500 iterations of applying neighbourhood predicates.
-	Reported times are averages of 10 runs.}
-\end{figure}
-
 \paragraph{Radiation} Our second experiment is based on a problem of planning cancer radiation therapy treatment using multi-leaf collimators \autocite{baatar-2011-radiation}.
 Two characteristics mark the quality of a solution: the amount of time the patient is exposed to radiation, and the number of ``shots'' or different angles the treatment requires.
 However, the first characteristic is considered more important than the second.
@@ -592,13 +583,15 @@ Given two variables \mzninline{exposure} and \mzninline{shots}, once we have fou
 Since each added lexicographic constraint is strictly stronger than the previous one, we never have to retract previous constraints.
 
 \begin{figure}
-	\centering
-	\includegraphics[width=0.5\columnwidth]{assets/img/6_radiation}
-	\caption{\label{fig:6-radiation}A run-time performance comparison between
-		incremental processing (Incr.) and re-evaluation (Redo) of 9 Radiation
-		\minizinc\ instances in the application of Lexicographic objectives on a 3.4
-		GHz Quad-Core Intel Core i5 using the \gls{gecode} 6.1.2 solver. Each test was run
-		to optimality and was conducted 20 times to provide an average.}
+	\begin{subfigure}[b]{0.5\linewidth}
+		\includegraphics[width=\columnwidth]{assets/img/inc_cmp_lex.pdf}
+		\caption{\label{subfig:inc-cmp-lex}Radiation}
+	\end{subfigure}
+	\begin{subfigure}[b]{0.5\linewidth}
+		\includegraphics[width=\columnwidth]{assets/img/inc_cmp_lns.pdf}
+		\caption{\label{subfig:inc-cmp-lns}GBAC}
+	\end{subfigure}
+	\caption{\label{fig:inc-cmp} A comparison of the two new incremental techniques and a recompilation strategy.}
 \end{figure}
 
 As shown in \cref{fig:6-radiation}, the incremental processing of the added \mzninline{lex_less} calls is a clear improvement over the re-evaluation of the whole model.