27 Mayıs 2011 Cuma

anlaşılıyor

GECCO'nun reddettiği (ve orta not verdiği) paper'i noktasına dokunmadan hesaplamalı mimarlık alanının önemli bir konferansına gönderdim (acadia). mimarlıkla ilgisi zayıf denmiş. doğru. ilerleyen versiyonlar ilgili olacak. "Out of more than 200 entries, we accepted 31 full and 9 work-in-progress
papers." demişler. böyle durumlarda alanla ilgisiz bir paper'in seçilme ihtimali pek yok. ama aslında öyle olumlu reviewlar yazmışlar ki keyifle aşağıya ekliyorum. çalışmayı beğenmişler. eleştirilerin çoğu da yerli yerinde. zamanla eğilmeyi düşündüğüm hususlar. oh keyfim yerine geldi. yapılan iş kendi bağlamında / alanında değerlendirildiğinde zayıf ve kuvvetli yanları daha doğru değerlendiriliyor. kısaca, anlaşılıyor.


----------------------- REVIEW 1 ---------------------
PAPER: 221
TITLE: Generating Layout Arrangements through an Adaptive Multi-Objective Evolutionary Algorithm
AUTHOR: [yazar adını direk yazmışlar!]

I enjoyed this paper and found the research to be interesting, however, the connection to architecture is only indirect. While the problems tackled in this research are relevant to genetic systems in architecture, the application developed here is in graphic design, not architecture, and this makes the paper too distant from the audience of ACADIA. If this paper included a series of architectural examples rather than t-shirt design examples, it would help me greatly to see its implications for architecture, which I think could be significant, but nonetheless remain fuzzy without such examples.

----------------------- REVIEW 2 ---------------------

I did see that any of the results from the methods evaluated had been reviewed by non-automated means to form a baseline.

----------------------- REVIEW 3 ---------------------

This paper explores an interesting application of well-know computational technique (evolutionary computation) to two-dimensional graphic design. The paper is well-written and the experiments performed are well-formulated. The authors clearly understand the technical issues involved in the use of evolutionary computation for design.

Furthermore, in the context of many examples of using evolutionary computation to solve technical and performance-based problems (for example, to design the nose cone of a high-speed train for aerodynamic performance), the paper identifies and investigates a very interesting and unique territory: using evolutionary computation to solve aesthetic problems.

More specifically, several aspects of the experiment details are compelling, including the micro-runs approach, the comparison of the micro-runs approach and the Pareto-based approach, the exploration of adaptive and self-adaptive parameter control, and the analysis of the efficiency and performance cost of different computational processes.

Yet, I believe there are some fundamental issues related to design and computation that the paper might benefit from engaging more fully.

First, as computation becomes more and more embedded in design processes, what are the implications of quantifying features of design such as aesthetics that have traditionally been defined as qualitative? What are the benefits of quantifying aesthetics? What are the limitations? Is producing novel designs the goal? If so, should we be more specific about defining novelty? Does quantifying (and optimizing) aesthetics challenge fundamental definitions of design and designers?

Second, in order to evaluate this design process, it may be helpful to compare it to a more traditional process. In other words, design of graphic layout through multi-objective evolutionary algorithms could be compared to design of graphic layout through direct human intelligence and intuition. What are the benefits and unique characteristics of the algorithmic approach? Again, is novelty the goal? If so, how can the experiment described in the paper demonstrate that the algorithmic approach has produced something novel or helpful compared to a non-algorithmic approach?

Third, in order to evaluate the experiments more specifically, it may be helpful to compare the resulting designs to a few predicted designs. In the use of evolutionary computation, it is very easy to inadvertently set up an experiment that produces only designs that are predictable or already known. The experiment may run successfully, but it will not be very useful. This is often the case when the experiment involves a narrow and simple design space. If the design space is too narrow and too simple, evolutionary computation is not helpful in exploring it. For the experiments described in this paper, I am concerned that their design space is too narrow and I encourage the authors to consider ways to make the design space more broad and/or more complex. More specifically, I am concerned that providing targets (color distribution target and design unit layout target) may overly constrain the design space. In other words, given such specific targets, it may be possible t
o create a hand sketch of the desired outcome without going through the algorithmic process. If the algorithmic process produces results that are substantially different than this hand sketch, or if it resolves issues that are very difficult to sketch (such as very precise fine-tuning of color or spacing), then it would be helpful for the authors to make this more explicit in the paper.

In addition to these major points, here are a few more detailed comments:

For the color fitness histograms, are they applied to the whole image, or on grid system?

For the population size and number of runs for each test, the numbers seem low, as the authors acknowledge. In some uses of multi-objective optimization, the rule of thumb is that the population size should be (2) x (number of inputs) x (number of outputs). Could some tests be run with larger numbers?

In future work, it may be interesting to combine aesthetic fitness criteria with other types of fitness criteria in order to create problems with a design space that is more complex and difficult to easily predict.

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