How does appearance of agents affect how people interpret theagents’ attitudes – Experimental investigation on expressing the same
information from agents having different appearance
Takanori Komatsu and Seiji Yamada

Abstract— An experimental investigation of how theappearance of agents affects interpretations people make of theagents’ attitudes is described. We conducted a psychologicalexperiment where participants were presented artificial sounds
that can make people estimate specific agents’ primitive
attitudes from three kinds of agents, e.g., Mindstorms robot,
AIBO robot, and a normal laptop PC. Specifically, theparticipants were asked to select the appropriate attitude basedon the sounds expressed by these three agents. The results
showed that the participants had higher correct interpretation
rates when a PC presented the sounds, while they had lower
rates when Mindstorms and AIBO robots presented the sounds,
even though these agents expressed information that wascompletely the same.

I. INTRODUCTION

R

ecently, one of the hottest topics in human-computerinteraction or human-agent interaction studies is “howdoes appearance of agents affect their interactions withpeople.” People are said to determine agents’ behavior
models based on the appearance of the agents so that theagents’ appearance significantly affects their way ofinteractions [1]. For example, when people encounter adog-like robot, they expect dog-like behaviors from this robot,
and they would naturally speak to it using commands andother utterances intended for dogs, such as “sit”, “lie down”,
and “fetch.” However, they do not act this way toward a
cat-like robot.
Several studies have focused on the effects of appearanceof agents on interactions with people [2,3,4]. For example,
Kiesler et al. [2] conducted a psychological experiment whereparticipants were asked to play a prisoner’s dilemma gamewith virtual characters (human and dog) that appeared on acomputer display. The results showed that participants who
had some experiences with owing dogs interacted
significantly more effectively with the dog-like virtual agent,e.g., cooperating with this agent significantly. Goetz et al. [4]investigated the effects on people’s impressions of different
designs of the head for humanoid robots. The results showedthat the participants answered that the robots with human-likeheads are good at social tasks, while robots with machine-likeheads are good at industrial tasks. It can be said that these
Manuscript received December 15, 2007.
Takanori Komatsu is with the Fiber-Nanotech Young Researcher
Empowerment Center, Shinshu University, Ueda, Nagano 386-8567, JAPAN(e-mail: tkomat@shinshu-u.ac.jp).
Seiji Yamada is with the Digital Content and Media Sciences Research
Division, National Institute of Informatics, Chiyoda, Tokyo 101-8430,
JAPAN (e-mail: seiji@nii.ac.jp).

were pioneering studies concerning about the effects ofappearance of agents on people’s impressions.
On the basis of the findings of these studies, we focused on
the issue how people interpreted the expressed informationfrom agents having different appearances, especially just after
these people have determined the agents’ behavior models
based on their appearances. Simply stated, we focused on therelationship between the agents’ appearances and theirexpressed information. As already mentioned in the above,
when people are face to certain agents, they determine theagents’ behavior models. They would then interpret theagents’ expressed information according to this agents’behavior model, and would start interacting with this agent.
Therefore, this study constitutes a significant step towardclarifications of the issue “how does appearance of agentsaffect their interaction with people.” Moreover, the result of
this study would contribute to a design policy of the agents,
such as “what kind of appearance should agents have in order
to interact with people effectively, and which kinds ofinformation should these agents express to people?”
II. RELATED STUDIES
Several approaches can be used to tackle with the issue
about the relationship between the agents’ appearance andtheir expressed information. For example, Matsumoto et al.
[5] proposed “Minimal Design” for designing the interactive
agents; that is, agents should only have minimum amounts ofappearance for users. In fact, they applied this minimal design
policy for developing their interactive robot “Muu” [6] andlife-like agent “Talking Eye” [7]. Although it can be said that
this study focused on the effects of appearance of agents on
people’s impressions which is the same as the Kiesler’s study[2] in the above, this study did not mention about therelationship between the agents’ appearances and theirexpressed information; otherwise, this study proposed arather abstract design strategy of the interactive agents.
Reeves and Nass showed in their “Media Equation” [8]studies that anthropomorphized agents or computers might
induce humans’ natural behaviors, like behaving toward
humans. It is said that their studies focused on people’scharacteristics in which people generally anthropomorphize
artifacts, even though these artifacts do not have human-likeappearances, so that these studies are not about therelationship between the appearance and the expressed
information.
Kanda el al. [9] conducted psychological experiments toobserve peoples’ behaviors toward two different types ofhumanoid robots ASIMO (Honda Motor Co., Ltd.) and1935
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978-1-4244-1823-7/08/$25.002008IEEE

Robovie (ATR-IRC). The results of this study showed thatthe different appearance of these two robots affected thepeople’s nonverbal expressions, such as gestures and body
movements. However, this study also did not mention about
the relationship between the appearance and the expressed
information.

III. AGENTS’ ATTITUDES, APPEARANCE, AND EXPRESSEDINFORMATION

Specifically, we conducted a simple psychologicalexperiment to investigate how people interpret presented
artificial sounds as expressed information to see if they could
determine specific attitudes from agents that have different
appearance, in order to clarify the relationship between theagents’ appearance and their expressed information.
We actually selected positive and negative attitudes
corresponding to valence values [8] as the primitive attitudes
that the agents should express. Informing people of these two
primitive attitudes is quite important if the agents are tointeract effectively with people [10].
Next, the artificial sounds that do not include any verbal
information but are simple and intuitive information forpeople were presented to inform people certain agents’primitive attitudes. Komatsu [11] showed that people canestimate different primitive attitudes by means of simple
beep-like sounds with different duration and inflections.
These simple but intuitive expressions are called as subtle
expressions [12]. Based on the results of this Komatsu’s study,
we speculated that agents with life-like appearances
expressing true-to-life information are sometime more
confusing to users and are not really effective for interactingwith people; Instead, there are a lot of cases that agents
without such life-like appearance expressing subtle
expressions are readily understood and are much more
effective [13,14]. So this is the reason why we utilized such
subtle expressions.
We then selected three artifacts for agents having different
appearance: a Mindstorms robot (The LEGO group), AIBO
robot (Sony Corporation), and a normal laptop PC (Let’s noteW2, product of Panasonic Inc.). These artifacts correspond to
making mechanical impressions of people, familial
impressions, and non-agent-like impressions, respectively.
As a concrete procedure of this study, at first, weinvestigated the specific artificial sounds that could makepeople evoke positive or negative attitudes (preliminary
experiment). We then conducted a psychological experimentto present the selected artificial sounds in the above
preliminary experiment from the three different agents and toinvestigate how people interpreted the presented sounds.
Finally, we summed up the results and discussed about theeffects of agents’ appearance on people’s interpretation of theagents’ expressed information.

IV. PRELIMINARY EXPERIMENT
Before the actual experiment, we conducted a preliminary
experiment to determine what kinds of artificial sounds as

1936

subtle expressions are effective to evoke certain attitudes,
positive or negative, from people. In this experiment, we
focused on artificial sounds that acted as subtle expressions in
the previous study [11]. We then investigated what kinds ofsounds were interpreted as being positive or negative
attitudes.
We prepared 44 difference types of triangle wave soundswith four different durations and 11 different F0(fundamental frequency) contours. Specifically, the four
durations were 189, 418, 639, and 868 ms. The 11 F0
contours were set so that the transition range of F0 values
between the onset and endpoint in the sound stimuli were
-125, -100, -75, -50, -25, -, 25, 50, 75, 100, and 125 Hz, andthese were linearly downward or upward (Fig. 1). All these 44
stimuli had the same F0 average of 131 Hz and the same
sound pressure (around the participants’ heads: 60 dB (FAST,A)). In addition, these sounds had a tone that sounds like acomputer’s beeping. Actually, these sounds were presented
by a normal laptop PC (Lets’ note W2, product of PanasonicInc.).

Fig. 1. 11 different F0 contours (duration: 189 ms). For example,“189u25” indicates that duration was 189 ms, and F0 transition rage
was 25 Hz with upward slope (increasing intonation).

A. Participants
Ten Japanese university students (6 men and 4 women;
19-23 years old) participated. Hearing tests established thatnone of them had any hearing problems.
B. Procedure
Firstly, an experimenter gave the instruction, like “Please
determine the attitudes of this PC based on the presented
sounds.” Participants were then asked to select one of thethree attitudes “positive,” “negative,” or “undistinguishable”
after presenting one of 44 prepared sounds. These three
attitudes were described to participants as follows:z Positive: the PC’s internal state appears to be good.
z Negative: the PC’s internal state appears to be bad.
z Undistinguishable: it is unclear whether the PC’sinternal state is positive or negative.
As a part of procedure, one randomly selected soundamong 44 prepared sounds was presented to participants.
Afterwards, the participants were asked to select one of the2008 IEEE Congress on Evolutionary Computation (CEC 2008)

three aforementioned attitudes. Each participant heard all 44
prepared sounds. The order of presenting sounds wascounterbalanced for all 10 participants.
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slope range. Also, among the five sounds interpreted as
negative attitude, we did the sound 418d125 due to the fastest
slope range. The rest of the remaining eight sounds were thenselected for agents with different appearance in the next
actual experiment.

V. EXPERIMENTThe purpose of this experiment was to investigate theeffects of the agents’ appearance on how participantsinterpreted the agents’ attitudes. Specifically, the participants
were presented the selected sounds in the preliminary
experiment by agents that have different appearances, e.g.,
the Mindstorms robot, AIBO robot, and the normal laptop PC(see Fig. 3). The participants were then asked to determine theappropriate attitudes among these three attitudes, i.e., positive,
negative or undistinguishable, based on the expressed sounds
made by these agents.

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C. Results
The result of this preliminary experiment was depicted inFig. 2 that indicates which sounds were interpreted as“positive (+),” “negative (-),” and “undistinguishable (*).”
The result showed that all 10 participants believed the PC had
positive attitude for five sounds, that is, 189 ms with an
upward slope range of 125 Hz (189u125), 418u125, 639u100,
639u125, and 868u125. Also, all they believed the PC had
negative attitudes for five sounds, that is, 418 ms with adownward slope range of 25 Hz (418d25), 418d50, 418d75,
418d125, and 639d50. Thus, the sounds with faster increasing
intonation regardless of the duration were interpreted as being
positive attitudes, while the sounds that have around 500 ms
with slower decreasing intonation were interpreted as being
negative attitudes.
Here, among the five sounds interpreted as positive attitude,
we eliminated the sound labeled 639d100 due to the slowest

Fig. 3. AIBO robot, Mindstorms robot, and the laptop PC (from left toright).

A. Participants
20 Japanese university students (17 men and 3 women;
19-24 years old) participated. These participants were notfamiliar with robots or these toys, and had not participated in
the former preliminary experiment. Hearing tests establishedthat none of them had any hearing problems.
B. Procedure
Firstly, an experimenter gave the instruction, like “This is amonitoring research to evaluate three agents by means ofquestionnaire.” The participants were then explained that theconcrete task was to select the one of the three attitudes“positive,” “negative,” or “undistinguishable” after the agent
expressed certain information. All participants experienced
the following three experimental conditions.
1. Eight
soundsexpressedbyMindstorms(MS-condition): the eight sounds came from an FM
radio tuner mounted on the Mindstorms. This radio
tuner received the transmitted sounds from a soundexpressing PC (Fig. 4).
2. Eight sounds expressed by AIBO (AIBO-condition):

2008 IEEE Congress on Evolutionary Computation (CEC 2008)

1937

8

the sounds were presented using AIBO’s operating
software “AIBO entertainment player” that was
installed in an AIBO operating PC. This software
offered to control the AIBO remotely.
Eight sounds expressed by the laptop PC(PC-condition): The nearly same conditions as thosein the preliminary experiment were used. This laptop
PC was remotely operated by a sound expressing PC.

3.

Interpretation Rates (tim es)

7

6

5

M S-condition
AIBO -condition
PC-condition

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Fig. 5. Average of Interpretation rate.

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FM radio tuner
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AIBO

Sound
AIBO
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expressing operating expressing
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Experimenter

Mindstorms

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displaying
monitorPC

Place of agent

Participant

M isinterpretation Rates (tim es)

7

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5

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Fig. 4. Experimental Setting.

1

All participants experienced these three conditions in
random order. In all three conditions, the eight sounds wererandomly presented to the participants. Actually, theexperiencing order of the experimental conditions and thepresenting order of the eight sounds were counterbalanced forthe participants.

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Fig. 6. Average of Misinterpretation rate.
8

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Undistinguishable Rates (tim es)

C. Results
We calculated the interpretation rates, which indicated how
many times the participants succeeded in correctly
determining the agents’ attitudes in all three experimental
conditions. The results were that the participants haveinterpretation rates of 4.45 for eight experimental stimuli in
the MS-condition, 4.40 in the AIBO-condition, and the 6.65
in the PC-condition (Fig. 5). The reason that the interpretation
rate in the PC-condition was lower than ones in the
preliminary experiment (the interpretation rate in the
preliminary experiment was 8.0 because we utilized thesounds that all 10 participants showed the sameinterpretation) was that there were two participants who
answered “undistinguishable” for the all experimental trial.
The results of ANOVA on the interpretation rates showedsignificant differences in these three experimental conditions(F(2,38)=15.56, p<.01(**)), and a multiple comparison using
an LSD test showed significant differences between the
PC-condition and the other two conditions (Mse=2.9421, 5%
level). Thus, these results showed that the participants’interpretation rates for the same sound stimuli differed basedon each agent’s appearances. Simply stated, the participants
showed the higher interpretation rate in the PC-condition,
while they did the significantly lower rates in the MS and
AIBO-conditions, even though the same sounds were
presented to participants in these three conditions.
Why did the MS and AIBO conditions show the lower

M S-condition
AIBO -condition
PC-condition

4

6

5

4

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M S-condition
AIBO -condition
PC-condition

3

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Fig. 7. Average of Undistinguishable rate.

interpretation rates? To tackle with this issue, we investigatedwhether the participants selected the opposite orundistinguishable attitudes, especially when the participants
selected the wrong attitudes. Specifically, we calculated themisinterpretation rates, which indicated how many times theparticipants failed to determine the agents’ attitudes correctlyin all three experimental conditions, and theundistinguishable rates, which indicated how many times
they selected the “undistinguishable attitude.” The results
were that, in the MS-condition (the interpretation rates of4.45), the participants had the misinterpretation rates of 2.05
and the undistinguishable rates of 1.50. In AIBO-condition(the interpretation rates of 4.40), they did themisinterpretation rates of 1.95 and the undistinguishable rates
of 1.65. In PC-condition (the interpretation rate of 6.65), they
did the misinterpretation rate of 0.30 and the2008 IEEE Congress on Evolutionary Computation (CEC 2008)

undistinguishable rate of 1.05 (Figs. 6 and 7).
The results of ANOVA on the misinterpretation rates
showed the significant differences in three experimental
conditions (F(2,38)=13.42, p<.01(**)), and a multiple
comparison using an LSD test showed significant difference
between the PC-condition and the other two conditions. Also
the results of ANOVA on the undistinguishable rates showedthat there was no significant differences among threeexperimental conditions (F(2,38)=1.04, n.s.). Therefore, it
was clarified that the lower interpretation in MS and
AIBO-condition was caused by the participants’misinterpretation on the presented sounds, not by their
undistinguishable answers.

VI. DISCUSSION
Eight artificial sounds selected in the preliminary
experiment were presented to the participants by a
Mindstorms, AIBO, and PC. The participants’ interpretation
rates, indicating how many times the participants selected thecorrect attitudes, were then investigated. The results were thatthe interpretation rates when a PC expressed these sounds
were significantly higher than the rates when a Mindstorms
and AIBO expressed them. This difference revealed that theagents’different
appearances
affectedpeople’sinterpretations of the agents’ attitudes, even though theseagents expressed information that was completely the same.
We also focused on the case of the MS and AIBO
conditions that the participants showed the lower
interpretation rates, and investigated whether the participants
selected the opposite or undistinguishable attitudes. The
results were that the lower interpretation in MS and
AIBO-condition was caused by the participants’misinterpretation on the presented sounds not by their
undistinguishable answers. This revealed that the agents’
different appearance caused the people’s misinterpretationsof the agents’ attitudes, again, even though these agents
expressed the same information.
First, let us consider why the PC condition showed thehigher interpretation rates compared with the other conditions.
One reason is that the presented eight sounds were selected in
the preliminary experiment that these sounds were presented
by a laptop PC, which was also used in the PC condition.
Thus, these sounds may have been effective only for
informing people of primitive attitudes when they were
presented by a laptop PC. This phenomenon may be rooted in
the fact that the PC expressing beep-like sounds was very
familiar for all participants, while MS and AIBO expressingthese sounds was unexpected and not familiar for them.
According to this familiarity between the agents’appearance and its expressed information, a Japanese robot
designer Sonoyama [17] argued that most people have strong
tendencies to expect higher abilities and functions on thevarious types of robots; that is, they expects the fluent speechsounds, smooth verbal conversations and so on. This
argument supports our argument mentioned in the
Introduction that “people are said to determine agents’behavior models based on the appearance of the agents.” In
our experiments, we designed that the Mindstorms and AIBO

robot just expressed the artificial sounds that were quite
similar with the beep sounds expressed by the normal
computers. Therefore, it would be an outside of the scopes forthe participants to receive such sounds from the robot so thatthey eventually would be disappointed by these robots. Thismight cause the lower interpretation rates on the MS and
AIBO experimental conditions.
Therefore, to clarify the relationship between the agents’appearance and its expressed information, it strongly requires
in investigating the relationship between the agents’appearance, these expected functions and their actual
functions. We then proposed “an Adaptation Gap
Hypothesis” to capture this relationship. This adaptation gap(AG) can be defined as follows:
AG = F − F
Here, F is the agent’s actual function and F is the agent’s
expected function based on its appearance. This hypothesis
can handle the following three situations.
I. AG = 0 (F = F ) : When the actual function equals to the
expected function. It is said that there is no adaptation gap.
In this case, the agent would be regarded as an instrument
for people.
II. AG < 0 (F < F ) : When the expected function isoverestimated compared with the actual function. It is said
that there is a negative adaptation gap. In this case, mostpeople would be disappointed by the agent and stop
interacting with this agent (Fig. 8, left).
III. AG > 0 (F > F ) : When the actual function exceeds the
expected function. It is said that there is a positive
adaptation gap. In this case, most people would not bedisappointed the agent and continue interacting with thisagent (Fig. 8, right).

Fig. 8 depicted the concrete example of this hypothesis. For
example, in the left of the Fig. 8, when one faces to thehumanoid robot, s/he would expect human-like behaviors
from this robot (higher F value). However, this robot
grasped a pen on the table with spending two or three minutes
or failed to detect her/his verbal commands frequently, s/he
eventually noticed this robot’s actual function F which isquite lower than F . In this case s/he would be disappointed
by this robot. On the other hand, in the right of the Fig. 8,
when one faces to the typical machine-like robot, s/he would
expect lower functions (lower F value). However, the robot
succeeded in behaving as s/he wants smoothly and s/he thennoticed the robot’s F which is higher than F . In this case,s/he would have good impressions on this robot and continueinteracting with this.
Here, it seemed that PC condition in our experiment might
fit to the case of AG = 0 (F = F ) and MS and AIBO conditions
would do the case of AG < 0 (F < F ) , and these situations
would affect the people’s interpretations of the presented
sounds. This interpretation would show why the MS and
AIBO conditions’ interpretation rates were lower thanPC-condition. We are then planning to conduct a
psychological experiment whether this adaptation gap
hypothesis can be observed in the relationship between

2008 IEEE Congress on Evolutionary Computation (CEC 2008)1939

various artifacts and people. The results of this consecutive
experiment would contribute the clarification of the issue“how does appearance of agents affect how people interpretthe agents’ attitudes.”F: I want total k w ithyou, s ir·
F: looks likepeople!

AG < 0 (F < F )F: “Hi!W hat’sup?”·
F: umm…nohope…

AG > 0 (F > F )REFERENCES[1]

S. Yamada and T. Yamaguchi. “Training AIBO like a Dog,” inproceedings of the 13th International Workshop on Robot and Human
Interactive Communication, pp.431-436, 2004.

[2]

S. Kiesler, L. Sproull, and K. Waters. “A Prisoner’s Dilemma
Experiment on Cooperation with People and Human-like Computers.”
Journal of Personality and Social Psychology, Vol. 79, no. 1, pp. 47-65,
1995.

[3]

S. Kiesler and L. Sproull. “Social Responses to “Social” Computers.”
In B. Friedman, Human values and the design of technology, CLSI
Publications, 1997.

[4]

J. Goetz, S. Kiesler, and A. Powers. “Matching Robot Appearance andBehavior to Tasks to Improve Human-Robot Cooperation”, inproceedings of the 12th IEEE Workshop on Robot and Human
Interactive Communication, 2003.

[5]

N. Matsumoto, H. Fujii, M. Goan, and M. Okada. “Minimal Design
Strategy for Embodied Communication Agents,” in Proceedings of the
14th IEEE International Workshop on Robot-Human Interaction(ROMAN2005), pp. 335-240, 2005.

[6]

M. Okada, S. Sakamoto, and N. Suzuki. “Muu: Artificial Creatures as
an Embodied Interface,” in Proceedings of 27th International
Conference on Computer Graphics and Interactive Techniques(SIGGRAPH 2000), pp. 91, 2000.

[7]

N. Suzuki, Y. Takeuchi, K. Ishii, and M. Okada. “Talking Eye:Autonomous Creatures for Augmented Chatting,” Robotics andAutonomous Systems, Vol. 31, pp. 171-184, 2000.

[8]

B. Reeves and C. Nass. The Media Equation, CSLI Publication, 1998.

[9]

T. Kanda, T. Miyashita, T. Osada, Y. Haikawa, and H. Ishiguro.
“Analysis of humanoid appearances in human-robot interaction,” inProceedings of the 2005 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS2005), pp. 62-69, 2005.

Fig. 8. Adaptation Gap Hypothesis.

And this would also contribute to generalize the Mori’s
uncanny valley theory [19] that as a robot is made morehuman-like in its appearance and motion, the emotional
response from a human being to the robot will become
increasingly positive and empathic, until a point is reachedbeyond which the response quickly becomes that of strong
repulsion. However, as the appearance and motion continue
to become less distinguishable from a human being, theemotional response becomes positive once more andapproaches human-to-human empathy levels.
VII. CONCLUSIONS
We conducted psychological experiments whereparticipants were presented artificial sounds that can makepeople estimate specific agents’ primitive attitude from threekinds of agents in order to clarify the relationship between theagents’ appearances and their expressed information. The
participants’ interpretation rates, indicating how many times
the participants selected the correct attitudes, were theninvestigated. The results were that the interpretation rates
when a PC expressed these sounds were significantly higher
than the rates when a Mindstorms and AIBO expressed them.
This difference revealed that the agents’ different
appearances affected people’s interpretations of the agents’attitudes, even though these agents expressed informationthat was completely the same. This result also showed that thelower interpretation in MS and AIBO-condition was causedby the participants’ misinterpretation on the presented sounds
not by their undistinguishable answers. This revealed that theagents’ different appearance caused the people’s
misinterpretations of the agents’ attitudes, again, even thoughthese agents expressed the same information.
It was then clarified that people’s interpretation werestrongly affected by the agents’ appearance and by the
expected functions based on their appearance. Therefore, toclarify the relationship between the agents’ appearance andits expressed information, it strongly required in investigating
the relationship between the agents’ appearance, these
expected functions and their actual functions. We thenpropose “an Adaptation Gap Hypothesis” to capture thisrelationship.

1940[10] K. Kobayashi and S. Yamada. “Informing a User of Robot' s Mind by
Motion,” in Proceedings of the 3rd International Conference on
Computational Intelligence, Robotics and Autonomous Systems(CIRAS 2005), 2005.
[11] T. Komatsu. “Audio subtle expressions affecting user’s perceptions,” inProceedings of 2006 International Conference on Intelligent User
Interface (ACM-IUI2006), pp.306-308, 2006.
[12] K. Liu and W. R. Picard. “Subtle expressivity in a Robotic Computer,”in Proceedings of the CHI2003 Workshop on Subtle Expressivity for
Characters and Robots, 2003.
[13] S. Yamada, and T. Komatsu. “Designing Simple and Effective
Expression of Robot’s Primitive Minds to a Human,” in Proceedings of2006 IEEE/RSJ International Conference on Intelligent Robots andSystems, pp.2614-2619, 2006.
[14] T. Komatsu, T., and S. Yamada. “How do robotic agents’ appearances
affect people’s interpretations of the agents’ attitudes?” In Extended
Abstract of the ACM-CHI2007 in work-in-progress session,
pp.2519-2525, 2007.
[15] T. Sonoyama, Introduction to robot design, mainichi communications,2006 (in Japanese).
[16] M. Mori. (1970). Bukimi no tani (The uncanny valley). Energy, 7(4),33–35, 1970 (in Japanese).

2008 IEEE Congress on Evolutionary Computation (CEC 2008)