GA2016 – XIX Generative Art Conference

Selections by the computer from the five years’ stock of Daily
Evolved Animations(Installation)
Topic: (Evolutionary Art, Evolutionary Critic)
Author:Tatsuo Unemi
Japan, Soka University, Department of Information Systems Science
www.intlab.soka.ac.jp/~unemi/
Abstract
As a challenge to make the machine that makes art, a machine in the author’s laboratory has beenworking to create ten art pieces everyday since October 2011 under the project named Daily
Evolutionary Animations [1]. Each of the pieces is a type of animation of 20 seconds composedwith abstract drawings as the frame images, evolutionarily selected based on a computational
aesthetic measures through 300 steps of generational changes. More than 18,000 pieces in total
have already been stocked in the server machine so far. Though the author carefully designed theevaluation criterion for the genetic algorithm implemented in the system, the results still do notalways satisfy the human’s aesthetics.
It would be a reasonable strategy to improve the fitness criteria as to fit more with human’s
aesthetic measure, but it is also nice to collect the better pieces from the stock according toevaluation by a type of Artificial Critic in order to save the five years of efforts by the system.
To design an appropriate evaluation function, the author choose a number of pieces from 920
pieces produced from January 1st to April 1st of this year, as both positive and negative examples
for training a machine-learning system. The target function is to calculate the grade point for eachpiece based on the values of the twelve elemental features used for evolutionary system of daily
production. We already have several types of methods to solve such type of optimization problem
such as Statistical Cluster Analysis, Artificial Neural Networks, Support Vector Machine, and so on.
Here the author employed a technique of Genetic Programming as the first trial. Throughthousands of steps for each of a number of different settings of the algorithm, it found a functionthat can grade all of 920 samples not perfectly but in acceptable level.
This installation is to display the selections from all of the pieces in the stock from October 14,
2011 to September 2, 2016. The selections contain 3,560 pieces that got higher grade by theevaluation function found though the above algorithm. Because the total duration for all of thesepieces is almost 20 hours, the installation will show the pieces in turn within the allowed hours inthe exhibition. The data for these animations are not in a form of movie file but program fragments
in a shading language of OpenGL. This feature is useful not only to reduce the size of data storage
but also to realize lossless images for each frame in high resolution even in 4K. It will be displayedusing a 4K monitor or a Full HD projector depending on the availability of the equipment and theenvironmental situation of the exhibition site.

unemi@iss.soka.ac.jp
Soka Univ., Tangi-machi
1-236, Hachioji, Tokyo,192-8577 JAPANKey words: evolutionary art, machine that makes art, artificial critic.
Main References:[1] Tatsuo Unemi, “Automated Daily Production of Evolutionary Audio
Visual Art - An Experimental Practice”, in Proc. of the Fifth Int. Conf. on
Computational Creativity, Session 2-2, Ljubljana, Slovenia, 2014.

Page 469

GA2016 – XIX Generative Art Conference

Selections by the computer from the five years’ stock of Daily
Evolved Animations
Prof. Tatsuo Unemi, BEng, MEng, DEng.
Department of Information Systems Science, Soka University, Hachioji, Japanwww.intlab.soka.ac.jp/~unemi/
e-mail: unemi@iss.soka.ac.jp

PremiseThe author has been conducting a project named Daily Evolved Animations since 2011 inwhich the computer automatically produces ten short animations everyday using atechnique of evolutionary computing. Though the author carefully designed the fitness
criterion based on computational aesthetic measures, it does still not always fit with thehuman’s aesthetic recognition. To save those more than 19,000 pieces stocked in theserver machine, he organized a genetic programming to find a function to select the betterones giving a collection of evaluations by himself as training examples. The installationshows a consecutive playback of those pieces of top 20%.

1. Automated evolutionary production of art pieces
Challenges to design a machine that can produce art have been conducted since 1980s,
as a famous pioneering work named AARON by H. Cohen [1]. Its successor, Shizuka by
K. Mukaiyama [2], is also driven by a set of rules that indicates how a human painterdraws an art piece. On the other hand, borrowing a power of computer that can calculatemassive computation much faster than human without any mistakes, evolutionary
computation has been employed as a technique for automated massive production
combined with computational aesthetic measures [3,4]. The author has also been
conducting such a project named Daily Evolved Animations [5] since 2011 in which thecomputer automatically produces ten short animations everyday. Though the author
carefully designed the evaluation criterion referring the previous works, it does still notalways fit with the human’s aesthetic recognition.

2. Artificial Critic
There are a lot of philosophical literatures concerning aesthetics in the human history. As
similar to finding what the nature is, we can find different ways of thinking of aesthetics.
The following part of this section describes short summaries of rationalism and empiricism
from a viewpoint of machine aesthetics.
2.1 Mathematical rationalism

Page 470

GA2016 – XIX Generative Art Conference

When we recognize something beautiful or ugly, we interpret the look as a type ofinformation. Beauty is a positive interest and ugliness is a negative interest. This meansthat one of necessary condition of beauty is how the stimulus from the object isinformative. A mathematician D. G. Birkhoff [6] proposed an equation that calculates theratio between signal and noise as an aesthetic measure that has been supported byresearchers of machine aesthetics for many decades. We feel no interest if the stimuli is
too simple or too complex. M. Bense [7] also wrote literatures on his philosophicalconsideration about aesthetics from similar point of view. A number of his successors tried
to produce art works following this idea, such as H. Kawano [8]. J. Schmidhuber [9] is anotable computer scientist and artist pursuing both theoretical and productive aspects
along this direction.
Such type of thought is to seek the universal law valid in general even without human. Thisidea must be important to consider the possibility of art by the machine for the machine,even if there is no aesthetic criterion sharable with human.
2.2 Cognitive empiricism
It is obvious that the phenomenal evidence of aesthetics is the cognitive function ofhuman. The approaches of soft computing to produce an optimized design fitting withhuman mind and emotion should be useful to develop a machine that makes art. Fuzzy
logic provides a formal framework to express informal intuition by human, artificial neuralnetworks, typically in deep learning, extracts hidden relationship between data, andgenetic algorithm constructs complex structure adapting to a complicated situation.
There are a number of challenges to estimate the human’s criteria of subjective preferenceby the methods described above, such as Yang Li [10]. It looks useful to design anindividual piece for the customer, but is not always helpful from a viewpoint of fine art
because the criteria should be a combination of a number of aspects strongly dependent
each other.

3. Genetic optimization of evaluation function
By the author’s observation on more than 19,000 pieces produced through the project ofDaily Evolved Animations, it is a mixture of wheat and chaff. Because this type of
evolutionary art makes care about only on the visuals in perception level but never onrecognition level nor interpretation level, what we need to consider is whether the image isinteresting or not as a visual stimuli.
The author organized a type of machine learning that predict if a given piece is good orbad, based on the statistical features of resulted frame images. The training data is acollection of examples on which the author judged it is good, bad or neutral. He observed
920 pieces produced from January 1st of 2016 to April 1st, then marked 158 good pieces,
113 bad pieces and 649 neutral pieces. He tried to organize a discrimination filter into two
categories, good and bad, by support vector machine. It worked well for the training
example, but it looks not so good when he checked it by cross validation to measure how
the learned boundary by the subset of training examples discriminates well the rest set ofthe examples.
Instead of discrimination filter, the author examined genetic programing, a type of
evolutionary computing that finds an optimal function expression. The target function is toPage 471

GA2016 – XIX Generative Art Conference

calculate the grade point for each piece. The fitness criterion is how well the grade point isuseful to distinguish good and bad pieces by assigning higher points to good pieces andlower points to bad pieces. The fitness F(f) is calculated based on the ranks r(f,p) of pieces
p in the list sorted following the grade points calculated by the candidate function f, as
shown in the following equations.

where P is a set of pieces of training example, m(p) is a label indexing human’s evaluation,
good, bad, or neutral, and α is a constant in the range (0,1). Each piece p is described bya vector of 12 scalar values of features that are used in automated evolution in SBArt [11].

Page 472

GA2016 – XIX Generative Art Conference

Figure 1. A sample monitoring view of evolutionary process to optimize a function that
calculates the grade point for each piece in the training example.
Figure 1 shows a sample of monitoring view of evolutionary optimization process in which
the population size is 600, generational alternation is by 1/3 selection [12], and mutation
rate is 1.75 divided by the gene length. After a number of trials of a variety of parametersettings for the process including local mating [12], an alternative model of generationalchange, a promising function was obtained as sown below.
exp(max(max(cos(-SE),min(max(0.370446+ME,max(max(sin(sin(log(ME))-(sin(log(-log(HH)))0.366921)),max(MT^cos(max(HH/max(GC,0.454969),ME/FT)),CP*cos(sin(0.776241/SA)))^0.761739),CP*log(MT*CE/EA))^CP),sin(0.7865+max(sin(GC+HI/GC),SS^cos(exp(ME)))))),max(sin(log(EA*(EAsin(exp(CP))))),max(ME,max(ME,max(sin(EA*(log(MT)*(EA/sin(GC)))),min(ME*MT,CE*(sin(sin(log(ME))-(0.125628-HH*HI))*(min(SA,SS)*exp(exp(EA)+0.261546))))*(-GC*sin(sin(SS))^max(cos(GCcos(exp(CP))),EA)))^0.135918)^sin(sin(sin(CP))))))^min(CP,log(ME^cos(EA))^cos(EA)))+0.71765/(cos(CP)+(0.135918-HH)^((sin(min(0.377795,SS))min(HH*CP/sin(EA),EA/GC))^((min(ME,CE)+(0.764454-CE))^CP/max(ME,0.341475))))The symbols of two capital letters in the above expression are an element of the vector
that describes the target piece. The summary is as follows.
HH = hue histogram, HI = intensity histogram, EA = edge angle distribution, GC = globalcontrast factor, CE = evaluation value of complexity, CP = estimated complexity, SE =evaluation value on saturation distribution, SA = average saturation, SS = standard
deviation of saturation, ME = evaluation value on motion factor, MT = motion factor, FT =original fitness value used in Daily Evolved Animation, where evaluation value means thefitness with the ideal value given by the author as the preset of Daily Evolved Animation.

4. Exhibiting the selections
It would be natural to embed the obtained function into evolutionary system for the projectof Daily Evolved Animation. Instead, the author used the function to organize selections
from 18,000 pieces that have been produced for these five years. These are stocked in theserver machine of the author’s laboratory. This means that the function took a role of an
artificial critic who recommends pieces to be exhibited. This is also good to save five yearsof the effort by the computer system. Sorting a collection of all pieces produced in theperiod from October 14, 2011 to September 2, 2016 by the grading function, the author
picked up the top 20%, that is 3,560 pieces, to be exhibited in turn. As each piece is 20
seconds animation, the total duration of playback is 20 hours.
The data for these animations are not in a form of movie file but program fragments in ashading language GLSL of OpenGL. This feature is useful not only to reduce the size ofdata storage but also to realize lossless images for each frame in high resolution even in4K. It will be displayed using a 4K monitor or a Full HD projector depending on theavailability of the equipment and the environmental situation of the exhibition site.
Figure 2 shows the frame image examples of top 10 pieces.
Page 473

GA2016 – XIX Generative Art Conference

Figure 2. Frame image examples of top 10 pieces.

References[1] H. Cohen: The further exploits of AARON, painter, Stanford Electronic Humanities
Review, 4(2), 1995.
[2] K. Mukaiyama: http://www.kazushi.info/?p=159[3] P. Machado, A. Cardoso: All the truth about NEvAr, Applied Intelligence 16 (2), 101–118, 2002.
[4] E. den Hijer, A. E. Eiben: Investigating aesthetic measures for unsupervisedevolutionary art. Swarm and Evolutionary Computation, 16, 52–68, 2014.
[5] T. Unemi: Automated daily production of evolutionary audio visual art – an experimental
practice, Proceedings of the Fifth International Conference on Computational
Creativity, Session 2–2, Ljubljana, Slovenia, June 9–13, 2014.
[6] G. D. Birkhoff: Aesthetic measure, Harvard University Press, Cambridge, MA, USA,1933.
[7] M. Bense: Einführung in die informationstheoretische Ästhetik, Rowohit, 1969.
Japanese translation by K. Kusabuka, Keiso-shobo, 1997.
[8] H. Kawano: Computer and aesthetics, Kagaku forum, No. 313, July 2010, 21–27,
Tokyo Science University (in Japanese).
[9] J. Schmidhuber: Low-complexity art, 1995.
http://people.idsia.ch/~juergen/locoart/locoart.html[10] Yang Li: Adaptive learning evaluation model for evolutionary art, WCCI (CEC) 2012,Brisbane, QL, Australia.

Page 474

GA2016 – XIX Generative Art Conference[11] T. Unemi: SBArt4 for an automatic evolutionary art, WCCI (CEC) 2012, Brisbane, QL,
Australia.
[12] T. Unemi and T. Koike: Evolution of a botanical development system in 3D euclidean
space, Proceedings of A-Life VI, MIT Press. 467–471, Los Angeles, CA, USA, 1998.

Page 475