![](bg6.png)
by changing the property values of each section in
the highest-ranked one. In the system, mutation is
not completely executed at random. We use eye-
catching evaluation and unconvincing evaluation for
the guide of mutation. The section which got the
highest value of eye-catching evaluation is the section
which attracted the viewers most. Therefore, to em-
phasize the section is important for making the con-
tent more attractive, and thus the system randomly
changes its graphical properties. The system does
not need to consider what kind of modification should
be done to the graphical properties for attractiveness,
because unattractive individuals will be exterminated
later. The section which got the highest value of
unconvincing evaluation is the section the matter of
which viewers thought unconvincing, and thus chang-
ing the content properties of the section is necessary
for making the content easy to understand, especially
in the case the section was the one which got the high
value of eye-catching evaluation. In the same way
as before, the system does not need to consider what
kind of modification should be done to the content
properties for making the section more convincing.
The system randomly selects the value from the pre-
defined values of the content properties. It is because
unconvincing sections will be selected for mutation
later again. Considering these guides from the eval-
uations, the system breeds some new individuals by
mutation in addition to those by crossover. The sys-
tem does not apply mutation to the crossovered con-
tent designs because we cannot identify the guide for
mutation of the crossovered content.
In Figure 6, the section about system overview
was emphasized because it gathered much attention,
and the sentences of the section about system func-
tionalities were replaced with other pre-defined sen-
tence patterns because they were thought to be un-
convincing.
4 CONCLUSIONS
In this paper, we have presented a novel method
which does not rely on manual evaluation and auto-
matically makes the content of digital signage more
adapted to the local tastes. We take advantage of
viewers’ involuntary behaviors in front of the digital
signage for evaluating the attractiveness of the con-
tent, and make the content design more attractive to
the viewers utilizing a genetic algorithm, which is
useful for solving problems for which no exact so-
lution is found. We empirically proved the feasibil-
ity of the method through the development of a pilot
digital signage system for displaying academic con-
ference posters.
The current system is a pilot system and can be
extended in many directions. One major point is how
the system gathers enough evaluations in places with
few people, and/or for short periods. We will seek
the best timing of changing the content design, and
combination of multiple digital signage devices. Be-
sides, we limited the usage of digital signage for only
a particular purpose, but we will be able to generalize
the method more extensively, considering various be-
haviors and modifications. For example, we can con-
sider the use of eye-catching evaluation and uncon-
vincing evaluation also in crossover of the GA, and
take advantage of hierarchical relationships between
each section of the content design more thoroughly.
ACKNOWLEDGEMENTS
The work is supported in part by a Grant-in-Aid for
the Leading Graduate School Program for “Science
for Development of Super Mature Society” from the
Ministry of Education, Culture, Sports, Science and
Technology in Japan.
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