WCCI 2012 IEEE World Congress on Computational Intelligence
June, 10-15, 2012 - Brisbane, Australia

IEEE CEC

SBArt4 for an Automatic Evolutionary Art
Tatsuo Unemi
Soka University
Hachioji, Tokyo 192-8577, Japan
unemi@iss.soka.ac.jp
http://www.intlab.soka.ac.jp/∼unemi/

Abstract—Owing to recent innovations in computer hardware,it has become possible to implement automated evolution to
produce interesting abstract images based on computational
aesthetic measures for fitness criteria within a feasible time using
a personal computer. The present paper proposes extensions ofaesthetic measures for the domain of evolutionary art, for both
images and animations. Using two small computers connected
via Ethernet, we realized an installation of automatic video artthat continuously displays a series of new interesting animations
on the screen by selecting individuals of higher fitness from an
evolving population in real time. The art project also includes
automated creation of animations that are accessible over theInternet. The project involves the automatic production of ten
20-second movies everyday and posting the digest movie to a
popular web site for movie sharing.
Keywords—video art, automatic art, real-time evolution, computational aesthetic measures.

I. I NTRODUCTION
Interest in beauty is an intrinsic psychological characteristics of human beings. One reason why the interactive
evolutionary computation (IEC) [1], [2] has generally been
applied to artistic productions is that because the evaluation
measure of aesthetics is assumed to depend strongly on a
subjective criterion for each individual person. Interactive
evolutionary computation is a powerful framework for theefficient exploration of a huge search space to find better
solutions that fit with the human-subjective criteria. A typicalearly challenge was the artificial evolution system implemented on a combination of a supercomputer and sixteen
graphics workstations by K. Sims in 1991 [3]. A number of
researchers and artists have attempted variations of encoding
methods, applications for other areas, and improvement of
human interface for convenience, for example.
On the other hand, similar to the case in which researchers
in artificial intelligence have been struggling to realize functionalities of human intelligence on the machine, the artistic
creativity of the machine has also been a target since the earlydays of computer science. One typical example is AARON,
which was developed by H. Cohen. The first painting generated by AARON was presented in 1973 [4]. AARON is anexpert system into which the knowledge of how to draw an
art piece is embedded.
Computational aesthetic measures have also been examined
as a useful technology for finding better images or analyzing
the styles of masterpieces in the context of computer graphics
and image processing [5]. These methods are also useful for
calculating the fitness values in order to automatically selectU.S. Government work not protected by U.S. copyrightfavorable individuals in evolutionary computation so as toobtain beautiful images. As one of the methods by which to
reduce user fatigue associated with the IEC approach, a combination of automated evolution was examined by P. Machado etal. [6]. Several different types of measures were also examined
for automated evolution by E. den Heijer et al. [7].
The present paper describes a new approach to embed
an automated evolution into a breeding system for abstract
images, SBART, developed by the author [8]. The first release in 1994 was runnable on UNIX workstations and was
extended and migrated to MacOS 8, MacOS X on PowerPC, and then MacOS X on Intel CPU. The recent extensions
include a real-time breeding of animation using the power ofparallel processing by GPU [9]. Similar to a number of otherrelated studies, the system uses a functional expression as agenotype that calculates a color value for each pixel to paint a
rectangular area. Several different types of measures borrowed
and modified from previous studies combined with newly
developed measures are introduced for fitness evaluation. In
addition to measures for a still image, a criterion for animationis also examined for a new functionality in order to evolve a
movie.
This extension enabled the installation of a type of automatic video art that continuously displays a series of new andinteresting animations on the screen by selecting individualsof higher fitness from an evolving population in real time.
The burden for a small computer to draw an animation inhigh resolution and to calculate the fitness value of each individual animation is heavy. By executing these two processes
separately on two machines, smooth real-time playback ispossible. The art project also includes the automated creationof animations that are accessible on the Internet. The project
involves the automatic daily production of ten 20-second
movies and posting of the digest movie to a popular web site
for movie sharing.
The following sections describe the aesthetic measures usedin this system, the method of generation alternation, theinstallation setups, and the organization of automatic net-based
art project.
II. A ESTHETIC MEASURE FOR STILL IMAGE
Aesthetic evaluation by an individual is fundamentally
driven by the cognitive system of that individual, the functionality of which depends strongly on personal experienceand cultural background. In order to implement such functions

2014via computer, it is necessary to use some type of learning
mechanisms to adapt to variations among individuals andto changes over time for an individual. Some researchers
are trying to embed such adaptability using algorithms ofartificial neural networks [6]. However, we avoid such anadaptive mechanism and consider only common criteria thata majority of people will agree on interestingness at the levelof perception. Therefore, abstract painting styles, such as apainting with a single color over the entire area of the canvas,
is beyond the scope of the present study, because knowledge
related to the historical and cultural context is usually requiredin order to interpret the piece appropriately.
The following subsections describe three measures for spatial arrangement and three measures for color variation. In
order to unify these measures in different dimensions for a
final fitness value, a normalization method is introduced, as
described in the final subsection.
A. Information theoretic complexity
The most extensively researched measure is information
theoretic complexity. Intuitively speaking, simple uniform
patterns, such as solid colors, should be eliminated from thecandidates for interesting images. Theoretically, Kolmogorov
complexity in information theory is an ideal concept to measure the degree of meaningfulness of the information containedby the data. However, the computation of this measure cannot
always be correctly completed in a feasible time and space.
For example, a pattern of pseudo-random dots is usuallydescribable by a simple algorithm and a few parameters, but
it is difficult to find the parameter values from the data even
if the algorithm is given. The alternative method by which toapproximate this measure is to use an algorithm of data compression, such as JPEG compression for a two-dimensional
image. P. Machado et al. examined various methods foraesthetic measure based on complexity [6], and J. Rigau etal. gave deeper consideration to complexity as an aesthetic
measure [10]. In this system, only one simple method has
been implemented, namely, to measure the distance between
the compression ratio of the given ideal value and the realvalue measured from the object image, just as E. den Heijer et
al. examined [7]. The new system was implemented using an
API of JPEG compression embedded in the MacOS X Cocoa
framework with the image quality parameter as 0.67. Since
the ideal value of the compression ratio should be variable
depending on the preference of the user, this system allows
the user to adjust the ideal value of the compression ratiousing a graphical user interface (GUI) described later herein.
B. Global contrast factor
E. den Heijer et al used the global contrast factor (GCF)[11] as an alternative measure of the interestingness of apattern [7], the algorithm of which was originally designed
to evaluate contrast as closely as possible to the intuitive
human measure. Contrast basically refers to the differencein brightness in a single image, but contrast is more thana simple statistical measure of variance among brightness

values over the pixels. Contrast should be referred to as highcontrast if the image is organized into only black and white
pixels without any intermediate gray pixels but should bereferred to as low contrast if the allocation of black andwhite for each pixel is randomly determined. K. Matkovic
et al. proposed an algorithm by which to calculate a weighted
summation among average differences over pixels for different
resolutions of a single image [11]. Their original method
uses a grayscale image of 1, 024 × 768 pixels as the original
image, and reduces the resolution by half in seven steps
until the smallest resolution of 4 × 3 pixels is obtained. The
weight values were statistically induced through psychologicalexperiments involving human subjects.
In order to expand the original method to be applicable toa color image, the difference between brightness is changed
into the Euclidean distance between color values in RGB color
space. Three component values are scaled in 2 : 3 : 1 for red,green, and blue to adapt to the characteristics of human eyes.
In the current implementation in SBART, the calculation
starts from half of the original size, i.e., 512 × 384, because
the computation time must be maintained short enough for theevolutionary process to be efficient. It is hoped that this issue
can be resolved by developing a more efficient algorithm usinga GPU.
C. One-dimensional brightness distributions
The above two measures consider the placement of colors in
an image but do not consider their two-dimensional distribution. In these measures, one-dimensional patterns, such as animage of parallel stripes, are also given a chance to gain highaesthetic score. Two-dimensional Fourier analysis should be
useful for evaluating such a pattern expansion over the image
but requires a long computing time of O(N log N) for eachfrequency. Another easy method by which to detect a patternof parallel stripes is to compare the variances of the brightness
distribution among rows and columns. If the image is a patternof horizontal stripes, the variance among rows is large, but thevariance among columns is zero.
The algorithm implemented here is used to calculate thedistances between distributions of brightness for the ideal
distribution and the measured distribution for each angle from0◦ to 90◦ stepping by 15◦ , transform each result value to adjust
zero to 1 and the furthest value to 0, then take the geometricmean among the values. Measurement for the variation ofdifferent angles is helpful for detecting the orthogonal parallel
stripes. The ideal distribution is extracted based on a statisticalanalysis over 1,000 snapshot photographs, similarly to the caseof a color histogram described in the following subsection.
D. Color histogram
In addition to the shape, some statistics on colors are also
important in order to index the characteristics of an image.
The frequency distribution of brightness was examined in aprevious study by E. den Heijer et al. using the distance
between the ordered frequency distribution and an ideal distribution based on Benford’s law [7]. Benford’s law states that

2015

0.4
Weighted Hue
Zipf’s law

0.3

Brightness
Benford’s law

0.2
0.1
0

1

2

3

4

5

6

7

8

9

Fig. 1. Comparison among the distributions of the average hue histogram
and the brightness histogram extracted from 1,000 snapshot photographs,
Benford’s law, and Zipf’s law.

the appearance frequency of digit d at the highest column inprime numbers follows the value of log10 (1 + 1/d) [12]. The
algorithm developed by E. den Heijer et al. counts pixels foreach span of nine grades of grayscale from black to white overall pixels in a single image, sorts the frequencies calculated
from the counted numbers in descending order, and thenmeasures the distance from the distribution of Benford’s law.
Another distribution found from the frequency of occurrence
for each word in a text is given by Zipf’s law, which statesthat the frequency is proportional to the inverse number of therank in descending order, i.e., the second most frequent word
appears half as often as the most frequent word, the third mostfrequent word appears a third as often as the most frequentword, and so on [13]. Both of these distributions can be foundin a number of natural phenomena.
In order to extend the method to be applicable to colors, we
use the distribution of hue values as another measure in addition to brightness. The differences from the case of brightness
are that the span of possible values is divided into 12 units
with one additional unit for gray and that each count value isweighted by the saturation of the color, i.e., the distribution of
the results is expressed by 13 real values. Instead of using theabove two well-known distributions, we investigated the average distribution among snapshot photographs of natural andurban sceneries and portraits. The results obtained using 1,000
photographs revealed similar shapes to the results provided by
Zipf’s law, but differ in the lower ranks, as shown in Figure 1.
Therefore, as the ideal distribution, we embedded a sequenceof frequencies induced from this investigation. The distance iscalculated by taking the summation of the absolute differences
between the ideal frequency and the calculated frequency foreach rank in the manner described by [7].
E. Favorable distribution of saturation
There is no universal aesthetic criteria on the tone of colors.
However, the user sometimes has a preference for a monotone
or color image. We introduce a user interface that allows theuser to indicate the ideal values of the average and standard

Fig. 2.

GUI for parameter settings of automated evolution.

deviation on saturation values over all pixels as a part of theparameter panel shown in Figure 2. A gray image becomes
preferable if both the average and the standard deviation are
low, and a colorful image becomes preferable if the average
is high.
When we define the range of saturation values within a fixed
span, such as [0, 1], the possible value of standard deviation islimited to within a range depending on the average value. The
standard deviation takes the maximum value when the sample
values are restricted in the edge values of the range, i.e., 0or 1. In this case, the average µ and the maximum standard
deviation σmax are:
µ
σmax

= P1
p
=
P0 P1

(1)
(2)

where P0 and P1 are frequencies of saturation values of 0
and 1, respectively, i.e., P0 + P1 = 1. A detailed derivation ofequation 2 is presented in Appendix. For convenience, theuser is allowed to input the value within [0, 1] as the standard
deviation, and the system multiplies the standard deviation
with σmax of equation 2 to obtain the actual ideal value. The
measure is a two-dimensional Euclidean distance between theideal values and the actual values extracted from the image.
F. Normalization and unified measure
It is necessary to develop a type of normalization for the six
measures described in the above subsections, because thesemeasures have different dimensions and cannot be directlycompared. We transform each measure in the range of [0, 1],
so that 0 indicates the worst measure, and 1 indicates thebest measure, and we map each measure to the normalizedmeasure in two stages, so that all of the processed values form
distributions of similar shapes. The first stage is a gamma
correction f1 (x) = xγ , so that the average value is equal to

2016

1.00

Original Data

0.75

TABLE IC ORRELATIONS AMONG MEASURES .

Brightness histogram
Hue histogram

Saturation

0.50

1D Brightness distribution

101 201 301 401 501 601 701 801 901

Global contrast factor

1

1.00

Normalized Data

0.75

0.50

Fig. 4.
1

101 201 301 401 501 601 701 801 901

the median value, i.e., γ = logm x̄, where m is the medianand x̄ is the average value of x. The second stage is a linear
transformation, so that the average and the standard deviation are adjusted to 0.5 and 0.2, respectively. The equation
is f2 (x) = (0.2/σ2 )(x − m) + 0.5, where σ2 is the standard
deviation among the transformed values by f1 . Combining
these two stages, the transformation function f is defined as:
0.2 logm x̄
(x− m) + 0.5.
σ2Sat.
0.02440.07850.04820.08540.0579GCF: Global contrast factor
Cmpl.: Complexity
Sat.: Saturation

0.50
0.25

0

0.25

0.50
0.75
Complexity

1.00

Scatter plot between global contrast factor and complexity.

G. Correlation between measures

Fig. 3. Distributions of aesthetic measures for 1,000 images produced fromrandom genotypes. Each measure is sorted in ascending order.

f (x) = f2 ( f1 (x)) =Cmpl.
0.0126
0.2591−0.25860.5907

0.75

0

0.25

0

GCF
0.00020.32920.15741.00

Complexity
01DBD
0.02040.0054

Hue H: Hue histogram
Brt. H: Brightness histogram
1DBD: 1D brightness distribution

Global contrast

0.25

Brt. H
0.0133

Hue H
Brt. H
1DBD
GCF
Cmpl.

Some of the measures may be useless if they exhibit strong
correlation. Table I summarizes the correlation factors between
two of the measures described above. The values used for
calculation are obtained after normalization. This shows that
there is no strong correlation except between the GCF andthe complexity. From the scatter plot in Figure 4, the areas ofhigh density are found in both the lower left and upper rightcorners. However, a high score for one of these two measures
does not always mean the score of the other measure will
be high. This suggests that both of these measures should be
included in the evaluation.
III. A ESTHETIC MEASURE FOR ANIMATION(3)

If the final value is out of the range of [0, 1], it is forced
to be revised to the nearest boundary 0 or 1. In orderto determine the coefficients in the transforming functions,
we examined 1,000 images drawn with randomly generated
genotypes. Figure 3 shows the distribution of each measureand its normalized version.
The final aesthetic measure for a still image is calculated as
the weighted geometric mean among these measures, where
the weights are adjustable by the user using a GUI in Figure 2.
The sliders for each measure allow the user to operate any ofthe measures at any time. Once one of these values is changed
manually, the others are automatically adjusted so as to ensurethat the summation of the weights remains 1 and to ensure thatthe ratio among the other weights stays same if possible.

One unique feature of SBArt4, which was released in
2010, is that the user is allowed to breed not only still
images but also animations in real time [9]. This used tobe difficult because the required computation time to renderone frame was much longer than the usual frame rate forsmooth animation. However, the recent improvement in GPUs
has made this possible. We tried to implement a measure offavorable degree of movement in animation to be combinedwith the measures for still images. Ideally speaking, this typeof measure should be constructed along with total evaluation
over the entire duration of the animation, but this seems
difficult because of the enormous computation power required.
As the first step of minimal functionality for this measure, we
implemented an algorithm to calculate the average difference
between consecutive frames among samples selected from the

2017

0.90

Fitness value

entire sequence of frame images. It is theoretically possible
to breed an animation of arbitrary duration, while ensuringthat the evaluation by the user remains easy, SBArt4 treats
relatively short animations, with a default duration of four
seconds. As usual, there is a trade-off between quick response
and precision. In order to ensure efficient progress of automated evolution, the current implementation uses ten samples
by default to estimate the average motion by calculating the
distance between color values for pixels of the same position
in a sampled frame and the pixels in the next frame. Because
the degree of motion should be adjustable based on user
preferences, the system provides a slider to allow the user
to set up the value at any time. The slider is located at thebottom right of the GUI, as shown in Figure 2.
The measure for a still image is also applied to each frame
image of ten samples. The total evaluation is calculated as theweighted geometric mean between the average measure of still
images and the average degree of motion in sampled frames.
The weight is also adjustable by the user using a slider at thebottom left of the GUI.

Best

0.60

Average

0.30

0

1

21

Fig. 5.

41

61

81 101 121 141 161 181 200
Steps

A typical evolutionary process.

IV. M ETHOD OF GENERATION ALTERNATION
From the viewpoint of the main objective of this system,
the generational change should be designed in with smallgrained alternation in order to ensure that the computation
time between generations is short enough for flexible interaction with the playback process. Among the several methods
available for this style of genetic algorithm, we select theminimal generation gap (MGG) model [14] because this model
provides the smallest grain size and effective exploration of alarge search space. In each step of generation alternation, thealgorithm performs the following steps:
1) Randomly elect two individuals from the population as
parents,
2) organize a family by producing two children bycrossover and mutation,3) select the best individual from four members in thefamily,4) randomly select another individual from the family,5) restore the two selected individuals into the population,and then6) discard the two individuals in the family that were notselected.
Random selections for both the parents and the second survivor
are effective in order to maintain broad diversity in the
population, and the selection of the best individual from thefamily has the same effect as the elitist strategy that guarantees
the best solution discovered in the process always remains in
the population.
Figure 5 shows a typical trend for the improvement of thefitness values in an evolutionary process. The first 20 steps
are used only to calculate the fitness values of the best 20
individuals given as the initial population imported from afield for breeding. The average values in the graph are foundfrom fitness values known among the population until the 20th
step and are later based on the 20 best individuals.

a. Twenty individual images of the initial population based on randomly
generated genotypes.

b. The best 20 individual images after 200 steps of generation alternations.
Fig. 6.
uals.

A comparison between populations of random and evolved individ-

Figure 6 shows an example of sets of individual imagesof initial and evolved populations. The population size is
80 but is initialized by 20 randomly selected genotypes andtheir children. The fitness values of individuals in the initial
population in the upper figure are from 0 to 0.65166, theaverage of which is 0.21530. These of fitness values improved
to a range of from 0.61281 to 0.82065, the average of which
is 0.68176, where the best individual in the initial population
remains.
We designed a GUI, shown in Figure 7, in order to makeit easy for the user to monitor the evolutionary process. The

2018

&RPSXWHU
6FULSW&RQWUROOHU

6%$UW

Fig. 8.

Fig. 7.

GUI for automated evolution.

middle part of the window displays the best 20 individuals
discovered in the process with each fitness value. The bottom
part shows a live animation of the trend graph of the best
fitness value and the average fitness value among the best
20 fitness values. Each measure of both the still image andthe animation is observable by a tool-tip balloon attached toeach view of the best 20 individuals that pops up when themouse cursor remains a number of seconds over the view.
The user is allowed to make individuals migrate between the
population for evolution and the field for breeding. These
functionalities are not necessary for automatic art, but are
useful for a combination of evolution and breeding.
V. S YSTEM SETUP FOR INSTALLATION
SBArt4 itself has the functionality to play an individual
animation in full-screen mode in order to display the production results. However, this consumes too much computational
power to be executed in parallel with the evolutionary process,because of usage conflict in both the GPU and the CPU. Fromthe viewpoint of artistic installation, it is better to keep theframe rate higher than 24 frames per second and to ensurethat the resolution is compatible with high-definition TV. In
order to achieve this requirement using reasonably priced
and sized personal computers, we examined a combinationof two machines connected by a LAN cable to execute theevolutionary process and the playback process separately. One
machine runs the automated evolution and the other manages
the playback of the animation, and the compiled code ofindividual to be played back is transferred from one machine
to the other, as shown in Figure 8.
We developed two new software applications. Controller&RPSXWHU(WKHUQHW

3OD\HU

System setup for installation of automatic art.

runs on the same machine on which SBArt4 is running, andplayer, which runs on the other machine. The main role ofplayer is to receive the text of a code written in shading
language in order to draw the animation on the hi-resolution
screen. Controller is a type of communication bridge between
SBArt4 and player and receives code from SBArt4 throughthe general pasteboard, which is usually used to copy andpaste between independent applications, and passes the code toplayer by means of a TCP/IP connection through the network.
Controller has a GUI to control player remotely in order tochange parameters for timing and sound effects.
For the automatic art, we developed software in AppleScriptthat supervises both SBArt4 and controller. SBArt4 was also
extended to accept the scrip commands to be controlled by
the script. The limitation of the size of the genotypes was
also introduced in order to prevent the infinite growth ofa functional expression through long-term evolution. In thecurrent setup, the number of symbols in a single genotype
is limited to 200. The script initiates an evolutionary process
in SBArt4 starting with a random population and selects 10
individuals after 50 steps in order to copy their codes into
a controller in turn. The script then commands SBArt4 toreconstruct the population by genetic combination among theindividuals in the current population. Some of the individuals
are replaced by new random genotypes in order to maintain
diversity in the population. The evolutionary process restarts
and continues until playback of all previous 10 individuals iscomplete. These processes are iterated an arbitrary numberof times in order to produce an ever-changing stream ofabstract animations. The duration of an animation for eachindividual is 20 seconds in a typical setting, in which theevolutionary process is allowed to continue for three minutes
and 20 seconds in order to produce the next 10 individuals forplayback. This is a sufficient time for a small computer, such
as Mac mini with a 2.5-GHz Core i5 and an AMD Radeon HD
6630M, to execute more than 50 steps of generational changes
in the MGG model described in Section IV.
A synchronized sound effect is also added to the animation.
The sound is synthesized with parameters extracted from astatistic analysis of frame images.
VI. AUTOMATED DAILY PRODUCTION
The functionality of automated evolution has enabled not
only an installation of automatic art but also automatedproduction without the assistance of a human. From October
6th, 2011, the system has been automatically producing 10
movies every day. The production procedure starts at 10 A.M.

2019

Japanese Standard Time, continuing the evolutionary processfrom a random population until the completion of 200 stepsof generation alternation. The procedure selects the 10 bestindividuals from the final population and generates 20-second
movie files for each with a synchronized sound effect.
Each of the produced movie files is compressed in both the
H.264 and Ogg formats in order to be adaptable for playback
by popular web browsers, such as Safari, FireFox, Google
Chrome, and Opera. These movies are accessible from http://www.intlab.soka.ac.jp/∼unemi/sbart/4/DailyMovies/. Reorganization of a web site to adapt to the newly generated movies isalso performed automatically just after the compressed movie
files are uploaded to the web server. The daily and weekly
digests of these movies are also posted to a popular site formovie sharing. The daily digest is a sequence of six-second
excerpts for each movie, for a total duration of one minute.
The weekly digest is a sequence two-second excerpts for eachof the 70 movies produced in the last seven days. These digests
are accessible at http://www.youtube.com/user/une0ytb/.
The entirety of the daily process is controlled by a program in AppleScript and requires approximately 40 minutes,
including evolution, movie file generation, compression, andposting. The daily process consumes an average of 346 MBof the storage in the web server everyday, which means thatstoring all of the movies produced over a number of years ona hard disk drive is feasible, because 126 GB for one year’s
worth of movies is not unreasonable, considering the HDD
capacity of currently available consumer products.
Figure 9 shows sample frame images of movies produced on
January 5th, 2012. These movies are accessible at http://www.
intlab.soka.ac.jp/∼unemi/sbart/4/DailyMovies/index.html?92.
VII. C ONCLUSION
We implemented an automated evolution based on a combination of aesthetic measures for fitness evaluation and minimal
generation gap for generation alternation in a breeding system
SBArt4. This type of approach has already been examinedby P. Machado et al. [6], but we added extended and newly
developed measures and a GUI for flexible combination among
measures. Evaluation of a favorable amount of motion in an
animation is a new challenge in this system. Although there
remain points that should be considered in order to improvethe effectiveness of supporting the production, we succeededin building a first trial of feasible implementation for fully
automated art using the power of a GPU. The method of
combining different measures is a subject for reconsideration.
Here, we used a geometric mean instead of a weighted
summation because measures should contribute as necessary
conditions. The use of fuzzy logic to calculate a membershipvalue between logical conjunction is an alternative method,where the smallest value among the measures is used as theresultant value of the combination. Some of the users might
prefer to use more than two measures as sufficient conditions.
The techniques to be examined in the near future areas follows: (1) Two-dimensional Fourier transformation andanalysis of the resulting spectra as one of the measures fora still image, and (2) optical flow for animation and analysisof the distribution of flow vectors as an alternative measure
for animation, and (3) information theoretical complexity of
three dimensional arrangement of boxels as another alternative
measure for animation. These ideas should be useful as methods for fitness evaluation but should be checked with respect to
how they consume the computation power in order to keep theelapsed time short enough for our purposes. The developmentof efficient algorithms will be examined in the future.
As applications of automated evolution, we organized an
installation of automatic art and a daily automated productionof movies for publication on the Internet. Although not examined in the present paper, another artistic application forlive performance [15] was realized and was performed at theGenerative Art Conference in Rome, 7th of December, 2011.
All of these projects remain in the experimental stage. We
hope that these projects will be accepted by the widest possible
audience.
The binary code of SBArt4, which is runnable on MacOS
X 10.6 or newer versions, is available from http://www.intlab.
soka.ac.jp/∼unemi/sbart/4/.
We hope the present research will inspire something new
in human culture, especially concerning the relation between
creativities of human and machine.
R EFERENCES[1] H. Takagi, “Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation,” Proceesings of the IEEE,
vol. 89, no. 9, pp. 1275–1296, 2001.
[2] H. Takagi and H. Iba, “Interactive evolutionary computation,” New
Generation Computing, vol. 23, no. 2, pp. 113–114, 2005.
[3] K. Sims, “Artificial evolution for computer graphics,” Computer Graphics, vol. 25, pp. 319–328, 1991.
[4] H. Cohen, “The further exploits of AARON, painter,” Stanford Electronic Humanities Review, vol. 4, no. 2, 1995.
[5] L. Neumann, M. Sbert, B. Gooch, and W. Purgathofer, Eds., Computational Aesthetics 2005: Eurographics Workshop on Computational
Aesthetics in Graphics, Visualization and Imaging, May 2005.
[6] P. Machado and A. Cardoso, “All the truth about NEvAr,” AppliedIntelligence, vol. 16, pp. 101–118, 2002.
[7] E. den Heijer and A. E. Eiden, “Using aesthetic measures to evolve art,”
in WCCI 2010 IEEE World Congress on Computational Intelligence,
Barcelona, Spain, July 2010, pp. 4533–4540.
[8] T. Unemi, “Simulated breeding: A framework of breeding artifacts on
the computer,” in Artificial Models in Software, 2nd ed., M. Komosinski
and A. A. Adamatzky, Eds. London, UK: Springer-Verlag, 2009, ch. 12.
[9] ——, “SBArt4 – breeding abstract animations in realtime,” in WCCI2010 IEEE World Congress on Computational Intelligence, Barcelona,
Spain, July 2010, pp. 4004–4009.
[10] J. Rigau, M. Feixas, and M. Sbert, “Informational aesthetics measures,”IEEE Computer Graphics and Applications, vol. 28, no. 2, pp. 24–34,
2008.
[11] K. Matkovic, L. Neumann, A. Neumann, T. Psik, and W. Purgathofer,“Global contrast factor – a new approach to image contrast,” in Computational Aesthetics 2005, 2005, pp. 159–168.
[12] J.-M. Jolion, “Images and Benford’s law,” Journal of Mathematical
Imaging and Vision, vol. 14, no. 1, pp. 73–81, 2001.
[13] G. K. Zipf, Human behavior and the principle of least effort. New
York: Hafner Pub. Co., 1949.
[14] H. Satoh, I. Ono, and S. Kobayashi, “A new generation alternation modelof genetic algorithms and its assessment,” Journal of Japanese Society
for Artificial Intelligence, vol. 12, no. 5, pp. 734–744, 1997.
[15] T. Unemi, “SBArt4 as automatic art and live performance tool,” in 14th
Generative Art Conference, Rome, Italy, December 2011, pp. 436–449.

2020

Fig. 9.

Sample frames from left to right of each of 10 movies from top to bottom produced automatically on January 5th, 2012.

A PPENDIX
From the definition of standard deviation,
2
=
σmax

1 N
∑ (xi − x)2 = x2 − x2 .
N i=1

Since xi = 1 or 0 and the expected number of xi having a valueof 1 is P1 N,
x2 = P1 and x2 = P12 .
Therefore,2= P1 − P12 = (1 − P1 ) P1 = P0 P1 .
σmax

2021