




\chapter{Conclusion}
\label{ch:Conclusion}
The final section provides a conclusion. Firstly, we discuss all the steps and outcomes even if they're procedural an unintentional. Secondly, we evaluate the whole study, addressing the weakness and experience from the experiment. Also we reconsider how to make the improvement. Lastly, we compare our \glsxtrshort{GA} to other \glsxtrshort{AI}s in general way, and generally think about the future of \glsxtrshort{GA}.



\section{Rewards along the way}
In the beginning, let's inventory what we gain through the whole experiment.
\begin{itemize}

	
\item \textbf{Databases:} to input into the program at beginning, in \cref{ch:Methodology_B}
\begin{itemize}
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	\item Database of materials' thermal performance
	\item Database of materials' economic value
	\item Database of materials' optical performance 
	\item Database of climate in the selected cities (including some future climate)
	\item \glsxtrshort{EU} norm of operation schedule, see \cref{Step IV: Operational Setting} \& \cite{ISO_15686-5}
\end{itemize}

\item \textbf{Parametric methodology to design the box:} in \cref{ch:Methodology for simulations,ch:Methodology_B}
\item \textbf{Program sectors:} independent \glsxtrshort{GH} script sectors in \cref{subsec:Step_V}
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	\item \glsxtrshort{EUI} simulation sector
	\item daylight analysis sector
	\item economic calculation sector
	\item \glsxtrshort{PMV} comfort simulation sector
\end{itemize}



\item \textbf{Wallacei engine \& data recorder} in \cref{subsec:Step_V,subsec:Step_VI}

\item \textbf{Visualizer \& cartographer} in \cref{subsec:Visualisation}

%\item \textbf{Result analysis}

\end{itemize}

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The above enumeration, lists all the outcomes during the process from our methodology. For sure, a large preparation was before the first gain. A long study has been made. Introduced in \cref{ch:Study of the Art}, we go over the field of \glsxtrshort{GA} approach in architecture regime and the field of EnergyPlus study. We developed a basic concept of our study.

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Having the direction, we studied the principle of the physics and economy, preparing theories for the simulation or evaluation. Ending the period of preparation, we enter the construction part.

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In the first part among the databases, we just collect the data from righteous sources. And in some cases, additionally, find a way to modify a part of them. Certainly, this part can be used as reference of the other study.

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Then in the second part, follow the methodology, we developed some sectors. They can be regarded as a sub-program, in other \glsxtrshort{GH} scripts if there're similar demands, these sectors can be deployed directly, running their tasks. I.e. they're highly reusable and are procedural gains.

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In the third part, we collect the optimized parameters combination. And we also developed the visualization sector to present the result matrix. This part of the program does excellent job on tubular graphic, it helps cartographer to make table in a more expressive way. However unlike the previous sectors, it's harder to deploy. The previous ones share the direct re-usability. The matrix maker need more adjustment to adapt the new running environment.

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In the end, apart from all the side-product, certainly, the real result and graphic are achieved at the end of the process. With this terminal outcome, we made the comparison and analysis. Attempting to gain some design principle from the solution found by \glsxtrshort{GA}, we also extracted the best sample among them, which can be used as design reference.



\section{Ability strengthened along the way}
In the previous section, we review all the procedure of our method, focusing on the intellectual property we've gained. And in this section we summarize the abilities we trained to complete the study.

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Here we list the skill that we gained from the study or that's strengthened.
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\begin{itemize}
\item \textbf{Academic skill:}
\begin{itemize}
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	\item Information retrieval 
	\item Bibliography management
	\item Academic writing 
\end{itemize}

\item \textbf{Theory learning:} 
\begin{itemize}
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	\item Economic realm: NPV calculation
	\item Thermological realm: heat transfer in opaque insulation
	\item Scenarios that predicting the future climate
	\item Psychological realm: \glsxtrshort{PMV} measurement
\end{itemize}

\item \textbf{GH scripting:} 
\begin{itemize}
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	\item HB model building
	\item HB energy simulation
	\item HB lighting simulation
	\item NPV calculation with C sharp coding
	\item LB \glsxtrshort{PMV} analysis
	\item Genetic algorithm employing
	\item Graphic composition in GH method
\end{itemize}

\item \textbf{Evaluation of the early design} 

\end{itemize}
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\textbf{Information retrieval \& Bibliography management:}

Just like ordinary study, during the considerable work of this study. Plenty of bibliographies and website were read and documented. Then during the writing, massive citations were put in the article. Actually, this is the essential academic skill. Using the LaTeX, this ability very fits our topic, because LaTeX is basically a scripting language. Our LaTeX is firstly running on Overleaf, then transferred to TeXstudio offline. So we're using the script tool to compose the whole article \textit{scripting architecture}.

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\textbf{Theory learning:} 

Most of the theories come from the material of our courses. So we just look up our books and choose the right formula to present. In addition to them, the prediction of future climate is a new-learned ability. Using the tool from Southampton University, we can transform any city's climate data into the future one. And we just learn how to use this tool with the macro on Excel.

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\textbf{Honeybee simulation}

It contain submissions in this aspect, i.e. \textbf{HB modeling}, \textbf{HB energy simulation}, \textbf{HB lighting simulation} in Honeybee package. The ability is develop by the tutorials on the internet. That involves the official guidance, online videos tutorials and discussion from forum. LB \glsxtrshort{PMV} analysis is a similar mission, which is easier and needn't engage the EnergyPlus software. Talking about EnergyPlus, it's a pity that our study material don't introduce its mechanical a lot. After this journey on \glsxtrshort{GH} platform with so many plug-ins, we can successfully conduct a program running on \glsxtrshort{HB}. However at some level, our knowledge of EnergyPlus is absent.


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\textbf{Scripting ability}

There're 2 places of C sharp coding that are introduced. The first one acts as the flow controller, while the second runs exponential function for the compound interest. Both of them are not difficult to develop. In flow controller refers to many Rhino document object and also transfers some commands. With the help of Rhino Common API \cite{RhinoCommon}, we learned plenty of method in searching the right tool from the developer and learned how the 3D software runs. On the other hand, we made the code for exponential function, then we asked ChatGPT to debug and make the lines more conventional. So our co-pilot coding is trained. These coding can be counted as additional side-products. But more importantly, the ability we gained is amazing.

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\textbf{Genetic algorithm employing}
It's a specific ability towards the optimization task. And we get precious experience from it which we will present in \cref{subsec:Improvement in settings}.

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\textbf{Graphic composition in GH method}
Our \glsxtrshort{GH} script is not only for running the experiment, also for the visualizing the result data. Our method follows the mainstream idea, that is to create the mesh by array, then create a gradient to color them. This is how the most of the colorful analysis graphic are made. This time we just scale it a bit into the largest version that we've made.


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\textbf{Evaluation of the early design}
During our design process, we usually spend little time on the early stage. Even is there's a long-term stage, it's likely to be masterplan-oriented. This time since we've simplified our master plan into an extreme, we have opportunity to consider the envelope in the early stage. It's an precious experience to find out the best combination without the other limit. From here, we developed a sense of how the optimized envelope should be like, if it's a envelope-oriented project.

\section{Analogy of the study}
Let's use the same analogy as in \cref{ch:Methodology for simulations}. We try to describe our study using dogs as the optimized objects. Firstly, we've studied the climate of the 10 cities around the world and built up 10 garden there with their typical climate characteristic. Then we learn the canine breeding theory, how to cultivate the dogs. The breeding refers to modeling method. Secondly, we learned how to measure the dogs' performance, such as, the height, the running speeding, the amount of food intake, the metabolism ratio, etc. The measurement part refers to the simulations. Thirdly, in all the gardens we save the best performing dogs for next generation and sadly eliminating the low-ranked individuals. This part refers to the \glsxtrshort{GA} we've employed. Lastly, when the selective breeding iterates up to limit, we study the latest generation, analyzing the genes from the best dogs. Then we make graphic to compare the genetic combination from our best dogs. So in the end, in these 10 gardens, we have 10 packs of dog. Among each pack, the dogs are either specialized in one performance or equally balanced whose genes are worth studying.


\section{Improvements can be adapted}
Having done the experiment, we've gained more experience in the simulation. We have a deeper thinking of the experiment. Especially, some improvements can be made for a better performance.

\subsection{Improvement in settings}
\label{subsec:Improvement in settings}


\subsubsection{\glsxtrshort{GA} setting}
As mentioned in \cref{subsec:Objective Space}, now the \glsxtrshort{GA} de-crowding function is not completely unleashed. So a bigger generation size and shorter iteration can be made.

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Also throughout the 2 whole months we were running the simulations, the program just unexpectedly broke out once. So its stability is confirmed. However, its capacity is still underestimated, 3 fitness value seem to be to simple.

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Talking about more fitness values, average weighted method is not the best choice, based on the human logic. Giving some fitness value a priority, evaluating it more important than the others, it's more convinced way. However, this concept can't be easily applied in both Octopus or Wallacei.


%\subsection{Future concern}
%\label{subsec:Future concern}


\subsection{Improvement in design}
\label{subsec:Improvement in design}

Firstly, our parametric generating method can be updated. In the current situation, the genes can be divided into 3 groups, i.e. \textbf{the opaque parameters, the transparent parameters, the schedule parameters}. 

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Comparing to the opaque parameters, the transparent parameters are more combined, e.g. the glazing type alone won't significantly affects the final performance. It works together with the \glsxtrshort{WWR} and others. In reality, these numbers may be packed up with more restriction. 

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And it's a weakness for our \textbf{schedule parameters}. As we only have 1 parameter in this group. Still being underestimated, the \glsxtrshort{GA} can deal with more. In the ideal condition, more set-points and the details of schedule should be introduced.


\subsection{Improvement in the visualization}
\label{subsec:Improvement in the visualization}

The Pareto front mark now provides insufficient information. As mentioned in \cref{subsec:Pareto front mark}, only the first appeared Pareto front individual will be marked. Which means in the end game, many repeated Pareto front individual is hard to distinguish. This should be updated into a new way. E.g. using 2 different marks, maybe one colored mark and one un-colored mark, symbolizing the first appeared or repeated appeared Pareto front independently.


\section{AI in architecture}
\label{sec:AI in architecuture}
While we were writing this thesis, it's the roaring 20s of \glsxtrshort{AI}. Its booming is due to the neural network. However, when we date back to 20 years before, when the \glsxtrshort{AI}'s growing was not rapidly, there were many solutions evolving at the same pace, where the \glsxtrshort{GA} is among them. 
The neural network became the fittest by several reasons: 

\begin{itemize}
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	\item The acceleration of GPU techniques
	\item The transformer system
	\item The diffusion system
	\item The easiest interface
	\item etc. 
\end{itemize}

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Throughout all these reason, I think the benefit from GPU is primary. It is a hardware-determined situation, whereas OpenAI is just lighting up a fire of commercialization. GPU is good at parallel computation which enhance the capability of neural network largely. While the other \glsxtrshort{AI} aren't benefited from that.

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But there are also weaknesses in the generative \glsxtrshort{AI}:

\begin{itemize}
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	\item \textbf{Need of training:} Nearly all the generative \glsxtrshort{AI}s need a huge neural network, which should be trained in advance.
	\item \textbf{Data of training:} The base of the program is machine learning, which mean the \glsxtrshort{AI} needs to learn from material, such as, the example done by human or some standards. Massive material will be needed to train a network. Without the help of the database, it's hard to get sufficient materials.
	\item \textbf{Computing power of training:} Once we have enough data, we need to think about which machine can conduct the learning activity. Computation ability is necessary and sometimes the electric power consumption is considerable.
	\item \textbf{Overfitting:} On one hand, when training the neural network, it sometimes fits into a special pattern too much. E.g. if we train a neural network to recognize a cat, when overfitting happens, it regards the Persian cat as the right type but neglect the other types of cats. The other hand, the neural network is hard to transit. The expert on recognizing cat needs re-training to recognizing the dogs.
	\item etc. 
\end{itemize}

All of these features might reflect on the usage among architects, but which is the most concern? To figure out that we must know what kind of \glsxtrshort{AI} architect need.

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There are plenty of jobs that \glsxtrshort{AI} can help come to my mind. But I would just discuss those can be done with technique of today. 

\begin{itemize}
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	\item Rendering mission: diffusion model
	\item Search and find the appropriate regulation: transformer model
	\item Optimization: genetic algorithm
\end{itemize}


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What will be the future of \glsxtrshort{AI} in architecture? My personal perspective is most of the job will be supported by the \glsxtrshort{AI} using neural network. With the booming of GPU products, the neural network will grow powerfully, becoming the best copilot of designers. 

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However, the optimization along, these kind of mission still be space for \glsxtrshort{GA} to live on. There's no doubt that the future of designers will be full of \glsxtrshort{AI}s. But will \glsxtrshort{GA} prevails? Some characteristics make it fit into the mission of optimization. 
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	\item Free from learning
	\item Free from GPU limit
	\item Easy to transit
	\item Follow the original design work flow
\end{itemize}

To employ the \glsxtrshort{GA} is simple for designer. It doesn't need to pre-train the neural network and the powerful computer to run. It takes longer time but can run on personal laptop. Most importantly, if the design object is create in parametric method, then the only thing need to add is the \glsxtrshort{GA} engine, which is easy to deploy and an isolated part, having little effect on the other parts.

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Having conducted the whole study and thinking about the future, is it meaningful to study \glsxtrshort{GA} in architectural field? Will \glsxtrshort{GA} compete with the neural network \glsxtrshort{AI}s? From my perspective the answer is yes. There will be space. Machine learning is a type of function acting like \textit{learning} in the end. Learning is imitating, it can't go beyond from its predecessors. ChatGPT can be expert on both lyrics writing and movie scripting. But it can't go beyond Eminem or shoot another Avatar.
Just as StableDiffusion can't be another Picasso. Maybe it can reach the elite level, beating 99\% of human, but it can't be the master which is the limit of imitating.

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However, when it comes to optimization. Genetic algorithms have different answer. The generative genes are created randomly. Which means some of the combination might never be discovered by human, it can create something totally new. That's the value of gene and so the genetic algorithm. The designers will need to find optimization, and \glsxtrshort{GA} is good at evolving in a complex multi-object environment.

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Just like the character Dr. Ian Malcolm said in 1993 film \textit{Jurassic Park}. 

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\textbf{"Life Finds a Way"}

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\textbf{La vita trova la sua via}
