Speeding Online Synthesis via Enforced
Selecto-Recombination
Shunsuke Saruwatari1 , Xavier Llorà1,2 , Noriko Imafuji Yasui1 ,Hiroshi Tamura3 , Kumara Sastry1 , and David E. Goldberg1
1Illinois Genetic Algorithms Laboratory (IlliGAL), Dept. of Industrial and Enterprise Systems Eng.
University of Illinois at Urbana-Champaign, Urbana IL 61801
2
National Center for Supercomputing Applications (NCSA)
University of Illinois at Urbana-Champaign, Urbana IL 61801
3

Research and Development Division, Hakuhodo Inc, Tokyo 108-8088, Japan.

{saru,xllora,niyasui,ksastry}@uiuc.edu, hiroshi.tamura@hakuhodo.co.jp,
deg@uiuc.edu
ABSTRACT

1. INTRODUCTION

Brainstorming has been greatly used as a method to generate a large number of ideas by variety of each participant’s knowledge. However, brainstorming does not always
work well because of spatial and communication limitations.
Moreover, brainstorming techniques present limited scalability. Meanwhile, genetic algorithms have been mostly regarded as an engineering or technological tool. However, theinnovation intuition suggests that genetic algorithms may bealso regarded as models of human innovation and creativity.
This paper focuses on online creativity sessions. Modelingthose creative eﬀorts using selecto-recombinative mechanism
can provide three times more novel ideas, increase the posting frequency by a 2.6 factor, and help overcome superﬁciality on online communications by favoring synthetic thinking.

Genetic algorithms have become a widely used tool for
problem solving in engineering and technology areas. The
advances in competent genetic algorithms [6] have made possible to solve quickly, reliable, and accurately problems of
unprecedented size [17]. However, genetic algorithms havealso entered areas ruled by aesthetic criteria; interactive genetic algorithms [18] are a clear exponent of collaborative
human-computer problem solving where no objective, but
subjective, function can be deﬁned. Moreover, social aspects of genetic algorithms have shown how they can act
as models of human innovation and creativity [12, 8, 13,20]—as postulated by Goldberg [5]. Human-based genetic
algorithms (HBGAs) target human process by drawing fromthe lessons learned from their computational counterparts.
Figure 1 summarizes the set of possible modes of cooperation between humans and computers by means of genetic
algorithms.
This paper continues the exploration of the social aspectsof genetic algorithms. Human-based genetic algorithms canbe metaphors of organizations, but also models of humaninnovation and creativity. Early eﬀorts have shown the beneﬁts of modeling creative processes after the evolutionary
metaphor [8, 13]. However, those eﬀorts lacked of quantitative analysis. In this paper, we focus on online creativity sessions. Usually mediated by some form of bulletin boards orthreaded discussion boards (TDB), creativity can be boosted
by modeling the online activities after a HBGA. Our work
will compare traditional TDB and HBGA-enhanced one toprovide a quantitative comparison of the beneﬁts introducedby the evolutionary modeling. We will show how an online
HBGA-based process can (1) provide three times more novel
ideas, (2) increase the posting frequency by a 2.6 factor, and(3) help overcome superﬁciality on online communications
by favoring synthetic thinking.
The rest of this paper is organized as follows. Section 2
describes the basic framework of the work presented in thispaper. Section 3 presents and reviews the details of a traditional TDB and its HBGA-enhanced counter part. Then,
section 4 presents the experimental setup, whereas section
5 analyzes the results obtained using both approaches andCategories and Subject Descriptors
H.4.3 [Information Systems Applications]: Communications Applications; H.5.3 [Information Interfaces andPresentation]: Group and Organization Interfaces

General Terms
Algorithms, Design, Experimentation

Keywords
Innovation, creativity, brainstorming, online discussions,human-based genetic algorithms, superﬁciality on online communication

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Computational
Human

Selection agent

Standard Genetic Algorithms

Interactive Genetic Algorithms

Computer-Aided
DesignHuman-Based
Genetic Algorithms

Computational

Human

Recombination agent
Figure 1: The human-computer interaction quadrant shows diﬀerent modes of genetic algorithms asmodels of human innovation.
the improvements achieved by using the evolution-inspired
TDB. The paper is concluded in section 6.

2.

CREATIVITY, INNOVATION, AND THE
ONLINE MEDIA

Starting in 1983, Goldberg [5] developed the so called fundamental intuition of genetic algorithms, or the innovation
intuition. Speciﬁcally, the innovation intuition of GAs isabout the work together of: (1) selection and mutation, and(2) selection and recombination. Moreover, the innovation
intuition of GAs provides a facet-wise modeling of humaninnovation. This approach models two orthogonal facets ofhuman innovation.
Selection + mutation = Continual improvement. Selection and mutation working together are a form of
hill-climbing mechanism. Mutation suggests variantsin the neighborhood of the current solutions; selection
acts as the decision process which accepts improving
changes with a high probability. This simple model
describes one of the facets of human innovation, the socalled continual improvement in total quality management literature, or as Japanese call it, kaizen.
Selection + crossover = innovation. Another facet ofhuman innovation is the so called cross-fertilizing innovation. People usually grasp a set of good solution features in one context, and a notion in another context
and juxtaposing them, thereby speculating that the
combination might be better than either notion taken
individually. Taking together selection and crossover,GAs are a computation model of cross-fertilizing innovation.
GAs also are main role players for the innovation technology revolution. As early mentioned, humans are to become the main measure of such a technology. Pervasive
GA-guided interaction between human and computers opens
a new research path to creativity- and innovation-support.
Two well-known models of such support are interactive GAs,and human-based GAs. Interactive GAs (iGAs) replace thecomputer computation of the relative ﬁtness of solutions
and the selection process by the judgment of a human evaluation. More detailed information about the progress ofinteractive GAs (iGAs) and interactive evolutionary computation (iEC) are presented in a review by Takagi [18].
Whereas iGAs replace the evaluation and selection by thehuman judgment, human-based GAs (HBGAs) [12] move
one step further and permit evaluation, selection, and variation to be performed by a human. For such reasons, the previous facets of GAs may be regarded as a ﬁrst order modelof human innovation—see Figure 1.
Traditionally, brainstorming has been greatly used as amethod to generate a large number of ideas by variety ofeach participant’s knowledge. However, brainstorming does
not always work well because of following limitations. At
ﬁrst, brainstorming has a spatial limitation. The spatial
limitation forces participants to gather at a same place anda same time. Brainstorming also has an expertise limitation.
The expertise limitation blocks participant’s group dynamics since diﬀerent knowledge and culture sometimes makediﬃculty of communication. Finally, there is a scalability
limitation in brainstorming. Human beings cannot communicate too many people at the same time. The scenario
gets more convoluted we those experiments are conductedon electronic or online media [10]. HBGAs can help eliminating some of these problems.

2.1 Brainstorming
The origin of brainstorming is the 1954 publication ofOsborn’s “Applied Imagination [15]”. He introduced brainstorming as a method, which enables groups to generate
more ideas than same number of individuals working separately because of synergy. However, subsequent studies
showed some problems in brainstorming such as evaluation
apprehension, and production blocking [1, 2]. The evaluation apprehension tends to occur for low-status membersof groups including high-status members. In the situation,
low-status members withhold their ideas for fear of suﬀering negative reactions from high-status members. The production blocking occurs when a participant cannot express
his/her ideas because of someone’s talking. While listening
to others’ ideas, the participant may forget his/her ideas.
To solve the problems, electronic brainstorming (EBS)
was developed [4]. In EBS, members in a group type ideas
anonymously and individually. The anonymity solves theevaluation apprehension, and the individual work solves the
production blocking. All participants can access group ideas
whenever they want. According to [4], EBS generated triple
number of ideas than conventional brainstorming when anumber of participants are twelve.
Santanen et al. [16] and Hender et al. [9] have exploredeﬀects of stimuli in EBS. According to [9], there are four
categories of stimuli:
1. traditional brainstorming (stimuli 1)
2. directory related to the problem (stimuli 2)
3. having associations to the problem (stimuli 3)
4. unrelated to the problem (stimuli 4)
The stimuli 1 are others’ ideas. The stimuli 2, for example, are multiple questions that give users to diﬀerent facets

of the solution space. The stimuli 2 generated about 133%
quantity of ideas than EBS [16]. The examples of the stimuli 3 are its cause, assumptions, attributes, and so on. The
stimuli 3 generated about 120% quantity and lower qualityof ideas than EBS [9]. An example of the stimuli 4 is analogy. The stimuli 4 generated about 80% quantity and better
quality of ideas than EBS [9].
These psychology approaches have improved brainstorming quality and tried to unveil the mystery of human innovation, but we are still in the middle of the puzzle. We still
do not know why EBS works well, why analogy works as
stimuli, whether or not there is more eﬃcient brainstorming variation, and so on. On the other hand, we alreadyhave learned computational innovation from GAs. We believe HBGA-enhanced brainstorming can give us a hint toprofoundly understand human innovation.

Progress bar

Input form

Generated ideas
Figure 2: Initial idea generation in SRBS. Whenthe user inputs his/her idea to the input form andpresses the add button, the posted idea appears inthe bottom area.

2.2 Human-based Genetic Algorithms
HBGAs outsource GA evolutionary operators to humanbeings [11]. Examples of HBGAs are Teamwork for a Quality Education (TQE), Free Knowledge Exchange (FKE), andAutomatic Concept Generation (ACE) [12, 7, 11, 3].
The TQE project is an application of the basic conceptsof genetic algorithms to create a more eﬃcient educational
environment [7, 12]. The participants of TQE are students,
faculties and staﬀ advisors. They interact each other according to the principles of GAs. Forty-two students participated a TQE pilot project in 1997, and student feedback
was generally good [7].
The FKE project is an evolutionary knowledge management web service [11]. Everyone can voluntarily participate
the FKE project. The participants can post problems, post
solutions, evaluate the solutions, and modify/recombine thesolutions like mutation/crossover in GA operators. Morethan 500 people from 92 countries attended the FKE projectin 7 diﬀerent languages in 1997 to 2001 [12].
ACE [3] is an email version of FKE. An organizer send aquery to the participants, the participants manually apply
genetic operators to the query, and reply it. The organizer
selects next oﬀspring according to the replies. ACE succeeded to decide the name of itself [3].
Since the HBGAs have performed positive results, we needto step forward for designing a competent HBGA, which enable us to innovate quickly, reliably, and accurately. Elaborate analysis and quantitative evaluation would be helpful
to get better understanding of HBGAs and to ﬁnd a connection between GAs and human innovation. Ueda et al.
[19] proposed a methodology for discovering building blocks
from text data. Our approach qualitatively evaluates how
the structural diﬀerence causes HBGA performance.

3.

TWO BRAINSTORMING SETTINGS

To ﬁnd a good balance point of human-machine combination in HBGA, we have developed selecto-recombinative
brainstorming (SRBS), which is a new brainstorming schemewith enforced idea selection and crossover. To evaluate SRBS
quantitatively, we have developed two diﬀerent discussion
components: (1) the SRBS and (2) the threaded discussion
board (TDB). Both components were built using the infrastructure provided by the DISCUS project, which is a project
to create a distributed and scalable environment for the integration of both human- and computer-generated knowledge in uncertain settings through eﬀective human-humanand human-machine collaboration [8]. The DISCUS projectprovides text processing features (stemming, stop word dictionary, morphological analysis, etc), analysis features (message length enumerator, new term enumerator, KEE [20],social network analysis, etc), and visualization features (barchart, plot chart, line chart, social network graph, etc). We
used the ﬂexibility and the features to make two discussion
components, and to analyze the brainstorming on the components.

3.1 Selecto-Recombinative Brainstorming
Selecto-recombinative brainstorming (SRBS) is more structured HBGA than threaded discussion boards (TDB). SRBS
has three phases, which let the participants in brainstorming process speciﬁc tasks, and the phases are initial idea
generation, idea selection, and idea crossover.
In the initial idea generation phase, each participant generates ﬁve ideas, which are related to an arbitrary theme.
Figure 2 shows a web interface on the initial idea generation
phase. The user inputs his/her idea to the input form, andpresses the add button to post the idea. Then, the posted
idea appears in the bottom area. In this phase, each participant can only see their own ideas, but cannot see otherparticipants’ ideas. However, each participant can know how
many ideas are posted through the progress bar placed in
the top area. If the progress bar represents 100%, whichmeans all participants have ﬁnished their ﬁve idea submissions, they move to the next phase.
The next phase is an idea selection phase. Each user individually votes for ﬁve ideas, which the user prefers, inpreviously generated ideas. Figure 3 shows a web interface
on the idea selection phase. The interface shows all previously generated ideas to the user, and each idea has avote button. If the user clicks the button, the interface removes the voted idea, and summarizes voting results in the
background. After all participants ﬁnished their vote, theprogress bar indicates 100%, and SRBS passes top ten voted
ideas to the next idea crossover phase.
In the idea crossover phase, the participants associate newideas from the combination of the previously selected ideas.
SRBS shows 10 C2 = 45 combinations of ten selected ideas inprevious selection phases to the user. The user generates a
new idea, which is related to each the combination. Figure
4 shows the web interface on the idea crossover phase. The
user ﬁlls the input form with new idea, which is related• Can ﬁnd easily if we lost it

Progress bar• Not need to bother remaining power.
Next is an idea selection phase. Each participant voted for
two ideas, which the participant thought it is good. Table
3.1 represents the voting result. According to the voting
results, the next generation is selected as

vote button• Be implanted to human body
• Personal authentication tool
• Communication speed is as same as current WiFi
• Not need to bother remaining power
Figure 3: Idea selection in SRBS. The user votes to
ﬁve ideas by clicking the vote buttons.

Progress bar

In the next idea crossover phase, each participant creates
new ideas, which is related to the combinations of the previously selected ideas. Alice created:• Be implanted to human body + Not need to botherremaining power = Drive using energy of human body
• Communication speed is as same as current WiFi +Not need to bother remaining power = Embed cellphone module to all devices

Stimulus

Stimulus
Input form

Bob created:• Not need to bother remaining power + Communication speed is as same as current WiFi = Standardization of rechargeable system• Personal authentication tool + Be implanted to humanbody = Biometrics

Figure 4: Idea crossover in SBRS. The user inputs
a new idea, which is the combination of the stimuli,
to the input form.
to two stimuli, and clicks the add button to post the idea.
When a total number of generated ideas reach to twenty, theprogress bar indicates 100%, SRBS passes the ideas to thenext idea selection phase, and participants proceed to thenext phase.
The idea crossover and the idea selection will continue
until a suﬃcient number of ideas being generated. SRBS has
many magic numbers: making “ﬁve” ideas in the initial idea
generation, top “ten” ideas in the idea selection, associating“twenty” ideas in the idea crossover, and so on. The numbers
are empirically derived, but under study.
As a simple example of how SRBS works, we consider abrainstorming of “ future cell phone”. This example is basedon an actual experiment using SRBS. Three participants,
Alice, Bob and Carol, attend the brainstorming.
In the initial idea generation phase, each participant generated two ideas. Alice created:• Personal authentication tool
• Communication speed is as same as current WiFi
Bob created:• Be implanted to human body
• Have interpreters
Carol created:

Carol created:• Personal authentication tool + Be implanted to humanbody = Touch and charge• Be implanted to human body + Communication speed
is as same as current WiFi = Brain-to-brain communication
These ideas move to an idea selection phase, again.

3.2 Threaded Discussion Board
Threaded Discussion Board (TDB) is conventional online
threaded bulletin boards added some real-time discussion
features with Ajax, and the features are user status notiﬁcation, discussion status notiﬁcation, and new message highlighting.
Figure 5 shows the screenshot of the TDB. The user status notiﬁcation is placed on the navigation bar. It notiﬁes

Table 1: Voting results. The popular ideas are selected as the next generation.
Idea
Personal authentication toolCommunication speed is as same as current WiFi
Be implanted to human body
Have interpreters
Can ﬁnd easily if we lost it
Not need to bother remaining power

Votes
1
1
3
0
0
1

New message posted
Figure 7: New post notiﬁcation on TDB. The notiﬁcation shows a number of new arrived messages
with Ajax.

Discussion
Navigation bar

Figure 5: An ongoing brainstorm session on TDB.
The navigation bar and the highlighting help thereal-time discussion.

users in TDB can communicate each other whenever theywant. We can say there is dense communication in TDB.
Following sections describe how the diﬀerences inﬂuence human creativity process.
SRBS and TDB have one common point: both of them
only use other people’s idea as stimuli. In SRBS, the users
make new ideas with other people’s ideas in an idea crossoverphase. The users in TDB might sometimes come up withnew ideas when the users read other people’s ideas in thediscussion. Both of SRBS and TDB do not use other extra
stimuli.

4. EXPERIMENTAL SETUP

User saru login

Figure 6: User status notiﬁcation on TDB. Each participant can know other participants presence withAjax.

the presence of the participants with Ajax polling (Figure6). The user can know who attend the discussion at the
moment. The notiﬁcation relaxes user’s feeling like talking
to a wall. The discussion status notiﬁcation is also placedon the navigation bar. The notiﬁcation shows a number of
new arrived messages with Ajax (Figure 7), and the users
can discuss seamlessly. New message highlighting works on
the discussion area. It highlights recent ﬁve messages. When
there are many threads and posts in the discussion, the users
have trouble to ﬁnd new posts. The highlighting helps theusers in such a situation.

3.3 SRBS vs. TDBSRBS and TDB have two diﬀerent facets: user tasks andcommunication. The user tasks in SRBS are more algorithmic than TDB. SRBS let the users proceed on to three speciﬁc tasks: initial idea generation, idea selection, and idea
crossover. On the other hand, the users in TDB can freely
discuss about their ideas. In spite of both of SRBS and TDB
being HBGA, SRBS is closer to GAs than TDB.
The task diﬀerence causes communication diﬀerence among
SRBS and TDB: sparse and dense. In SRBS, the users justneed to ﬁnish assigned tasks, but cannot talk each other.
Since the users share their ideas at transition from one phaseto another phase, we can say there is some communication
among participants, but it is sparse. On the other hand, theTo evaluate creativity diﬀerence in SRBS and TDB, one
brainstorming scenario “what is future cell phone” was used
for this experiment. Ten engineering graduate students at
the University of Tokyo participated in two ﬁve-person brainstorming groups: SRBS and TDB. All students attended
the experiment from a web browser with their own PC at
diﬀerent locations, such as their homes, laboratories. The
participants were randomly assigned to the groups, and aseparate brainstorming activity was started for each group.
The participants did not know the identity of their groupmembers. The following instructions were provided to thegroups:Please generate ideas about “future cell phone”.
The ideas are what kind of service, gadget, andsocial phenomenon will emerge.
ex) Apple will release an extrathin multitouch
cellphone.
Additionally, the brainstorming rules [15] were provided toonly TDB group:• Focus on quality• No criticism• Unusual ideas are welcome• Combine and improve ideas
Each group brainstormed for a period of 40 minutes in Japanese.

5. RESULTS
We demonstrate evaluation of HBGA according to settings by comparing SRBS and TDB. We ﬁrst verify creativity of SRBS and TDB with counting a number of unique
ideas in both of them. To count the number of unique ideas,
we manually remove duplicated ideas from that same groupand oﬀ-topic comments. A total number of ideas at eachtime are shown in ﬁgure 8. As shown in the ﬁgure, SRBS
acquired 3 times more ideas than TDB.
To ﬁgure out why SRBS can achieve such high performance, we compared the brainstormings deeply. SRBS and

2500

BBS
SRBS
y = 2.59x - 1.66
y = 0.87x + 0.06

Total message length, characters

Total ideas, count

150

100

50

0

BBS
SRBS
y = 53.25x - 177.29
y = 45.96x - 63.42

2000

1500

1000

500

0
0

10

20
Time, minute

30

40

0

10

20
Time, minute

30

40

Figure 8: A total number of ideas by time-line.
SRBS can acquire up to 3 times more ideas than
TDB in same time period.

Figure 9: Total message length by time-line. SRBS
and TDB have the same message length in sametime period.

TDB have the same total message length and a total number of new terms as shown in ﬁgure 9 and 10. The total
message length is a total number of characters containedin whole posts at each moment. The total number of newterms is a number of unique terms contained in whole posts
at each moment. The terms are extracted with morphological analysis built in DISCUS. Among SRBS and TDB,
there are not big diﬀerence in the total message length anda number of new terms.
Here we deﬁne a divergence ratio:

where m is the total number of ideas, and n is the total
number of new terms. The chemistry ratio represents how
eﬀectively terms are fused. The SRBS chemistry ratio is:rdivergence =nlwhere n is the total number of new terms, and l is the total
message length. The divergence ratio represents how divergent is the discussion. A divergent discussion makes more
new terms than a convergent discussion. The SRBS divergence ratio is:rdivergence,SRBS =7.79t= 0.14653.25twhere t is time. The TDB divergence ratio is:rdivergence,T DB =6.32t
= 0.138.
45.96tTherefore, the divergence ratio of SRBS and TDB are almost
the same:rdivergence,SRBS ∼= rdivergence,T DB .
Although SRBS and TDB have almost the same amountof the divergence ratio, why SRBS can generate more ideas
than TDB? To clarify this, we also deﬁne a chemistry ratio:rchemistry =mnrchemistry,SRBS =2.59t= 0.332.
7.79t

The TDB chemistry ratio is:
0.87t= 0.138.
6.32t
Therefore, SRBS fuses terms more eﬀectively than TDB:
rchemistry,T DB =rchemistry,SRBS > rchemistry,T DB .
We also examine the user activity diﬀerence between SRBS
and TDB. A total number of posts are quite diﬀerent among
SRBS and TDB as shown in ﬁgure 11. SRBS has 2.6 times
more posts than TDS in same time period. Figure 12 shows
message length of each post and average message length inbefore-and-after 5 minutes. SRBS has shorter average message length of each post than TDB. Hence, SRBS shortens
each message length and each message posting cycle. The result represents SRBS eliminates superﬁciality of brainstorming.
Additionally, SRBS can also be improved. SRBS used
38% of time for idea selection as shown in ﬁgure 13. Previous
studies on modeling user preferences to speedup interactive
genetic algorithms have shown to slice up to seven times theamount of time users spent evaluating solutions. A detailed
description is beyond the scope of this paper. A detailed
description of such a technique (active interactive genetic
algorithms) can be found elsewhere [14].

6. CONCLUSIONS & FUTURE WORK
We proposed a new brainstorming scheme called as selectorecombinative brainstorming (SRBS), which enforces idea

150

BBS
SRBS
y = 7.79x - 1.51
y = 6.32x - 3.16

300

Total posts, countTotal new terms, count

400

200

100

50

100

0

0
0

10

20
Time, minute

30

40

Figure 10: A total number of new terms by timeline. SRBS and TDB have almost the same numberof new terms in same time period.
selection and crossover on the users. We quantitatively compared the SRBS with brainstorming on conventional online
threaded discussion board (TBD). By modeling creative effort using selecto-recombination, SRBS can provide threetimes more ideas than TDB.
We deﬁned a divergence ratio and a chemistry ratio toﬁgure out why SRBS works well. The divergence ratio represents how divergent the discussion is, and the chemistry
ratio represents how eﬀectively terms are fused in the discussion. Our analysis described SRBS and TDB have almost the same amount of the divergence ratio, but SRBS
has a higher chemistry ratio than TDB. We also showed
SRBS eliminates superﬁciality of discussion with SRBS having shorter posting cycle and message length than TDB.
Our next step can be three ways. First, as shown in section 5, SRBS can get more speed because SRBS used 38%
of time for idea selection and the time can be reduced. Second, we can devise stimuli for idea crossover. SRBS in thispaper only used other people’s ideas as stimuli like traditional brainstorming. However, other stimuli, such as analogy, might increase idea quality and quantity as describedin [9]. Finally, we should investigate scalability of SRBS.
This paper evaluated SRBS on only a ﬁve-person group. Of
course traditional face-to-face brainstorming does not workat 100-person groups, but SRBS might be able to exceed thegroup size limit with certain techniques as GAs could [17].

7.

BBS
SRBS
y = 3.38x - 3.61
y = 1.30x - 2.01

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0
0

10

20
Time, minute

30

40

Figure 12: Message length per message and moving average message length in before-and-after ﬁve
minutes. The x-axis is a moment, on which the message is posted. The y-axis is a number of characters,
which the message has. SRBS has shorter average
message length than TDB.
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