Stimulating designers' creativity based on a creative evolutionary system and collective intelligence in product design

lable at ScienceDirect

International Journal of Industrial Ergonomics 40 (2010) 295–305

Contents lists avai

International Journal of Industrial Ergonomics

journal homepage: www.elsevier .com/locate/ergon

Stimulating designers’ creativity based on a creative evolutionary systemand collective intelligence in product design

Ji-Hyun Lee a,*, Ming-Luen Chang b

a Graduate School of Culture Technology, KAIST, Daejeon 305-701, Republic of Koreab Graduate School of Computational Design, National Yunlin University of Science & Technology, Touliu, Yunlin 640, Taiwan

a r t i c l e i n f o

Article history:Received 9 January 2009Accepted 4 November 2009Available online 3 December 2009

Keywords:Affective responseCollective intelligenceCreative evolutionary systemProduct form designGenerative design system

* Corresponding author. Tel.: þ82 42 350 2919; faxE-mail address: [email protected] (J.-H. Lee).

0169-8141/$ – see front matter � 2009 Elsevier B.V.doi:10.1016/j.ergon.2009.11.001

a b s t r a c t

In the field of industry design, the affective response characteristic can plays a significant role that couldgrasp customer’s attention in product. Especially, the design strategy of ‘‘affection-prerequisite’’ showsthe importance of the trend of customer-oriented for the industry products. Up to date, in considerationof the rapidly life-cycle on industrial design field, however, how designers can retrieve customer’saffective response promptly and using such information into their design work efficiently are significant,but not discussed yet. Based on this concept, the objective of this research is to present a creativestimulation system for designers. The ‘‘wisdom of crowds’’ was collected via the Web in order toascertain customers’ affective responses to product shapes. This data was used to create an evolutionarydesign system platform by using a design alternatives generation mechanism. The study involvedintegrating an interactive genetic algorithm into the mechanism to generate an interactive creativestimulation system. An affective design of the shape of a mobile phone was used as an example ofimplementation and also used as a proof of the research concept.Relevance to industry: HIGA and ESC methods proposed in this research provide the usage of the affectiveevaluation system for acquiring the customers’ affective responses in the conceptual design process ofa mobile phone. This design-generation process allows the design stakeholders to realize theircustomers’ affective responses interactively with sharing floating data among them according to the dailyupdate of customer’s evaluations.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

In current high competitive markets, customer-oriented designis of great concern to most companies [Jiao et al., 2006; Cross, 2000;Chou, 2004; Petiot and Yannou, 2004]. When customers havecontact with a specific product, the shape can evoke specificaffection, which is a common psychological response of a customerto the semiotic content of a product [Demirbilek and Sener, 2003;Khalid and Helander, 2004]. Crozier (1994) indicated that psycho-logical responses to products are influenced by the product’sappearance. For this reason, integrating customer affect into theprocess of product shape manipulation is a new trend called ‘‘Formfollows Affection.’’ [Chang and Lee, 2007a] A lot of literature nowexists that can help shed light on the relationship between affectiveresponse and product shape [Barnes et al., 2003; Chen et al., 2003;Chuang et al., 2001; Demirbilek and Sener, 2003; Hsiao and Chen,2006; Hsu et al., 2000; Jiao et al., 2006; Khalid and Helander, 2004;

: þ82 42 350 2910.

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Petiot and Yannou, 2004]. However, few studies have focused onthe effect of how designers can integrate customers’ affectiveresponse into their design creativity or processes systematically.

With the advent of the Web, the Internet has evolved into a userparticipation medium capable of high speed, on demand informationdelivery. The opportunities for social participation presented by theWeb should not be overlooked. Collective intelligence is a shared orgroup intelligence that emerges from the collaboration and compe-tition of many individuals [Russell, 2000]. We need the concept,collective intelligence that is a coherent integration of our diversitythat is greater any or all of us could generate separately [Atlee, 2003],to gather on demand information from customers, to stimulatedesigners’ creativity and to enhance their designs based on collectivewisdom of the customers via a computational mechanism.

According to Holtzschue and Noriega (1997), design process isproblem-solving. They describe that ‘‘design process is initiated bya problem, moves through creative thinking and design develop-ment, and ends in a design solution – a product.’’ Nowadays the useof evolution for creative problem solving is one of the most excitingand potentially significant areas in computer science. Especially,the creative evolutionary system (CES) is the latest software

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Fig. 1. Diagram of the research concept.

Table 1Comparison between Collective Intelligence and Wisdom of Crowds (redrawn fromLee, 2007).

Collective intelligence Wisdom of crowds

Similarity � Powered by decentralized user engagement� Accuracy controlled by the volume of data� Emphasis on information aggregation

mechanismMain purpose Knowledge production Problem solvingProblem solving Abstraction and

enhancementSocial proofing

Independentamong users

High Degree ofInteractivity

Isolation among

Typicalapplication

Wikipedia RecommendationSystem

J.-H. Lee, M.-L. Chang / International Journal of Industrial Ergonomics 40 (2010) 295–305296

solution for the relatively unexplored area of human creativity[Bentley and Corne, 2002a]. In design domain, evolution also canexplore a search space for novel designs [Bentley and Corne,2002b]. Janssen et al. (2002) described how generative evolu-tionary design tool provides guidance and inspiration for designersto help the designers’ creativity.

The purpose of this paper, therefore, is to develop a CES platformfor supporting designers to present a symbiosis concept that inte-grates collective intelligence and the recent research on affectiveresponse to product shapes. We collect the ‘‘wisdom of crowds’’ viaWeb to get on demand customers’ affective response to productshapes and utilize the information for a CES platform throughdesign alternatives generation mechanism. The research concept isshown in Fig. 1.

2. Related work

2.1. The measurement of customers’ affective response toproduct form

As we mentioned in Chapter 1, product appearance playsa significant role in conveying a designer’s ideas and provides thecustomer with visual references for affective response. Customerswho are inexperienced with a product may focus primarily on theirfirst impression and the styling of the product; they expecta product to be a living object that expresses an emotional imagevia its shape [Hsiao and Chen, 2006].

Subjective assessments are commonly used to evaluate affectiveresponse (e.g., asking people how they feel and what they like). It isimportant, however, to conduct such assessment in a structuredmanner so that the results are reliable and valid and can be comparedacross different products and cultures [Khalid and Helander, 2004].To investigate the customer’s perception, feeling, and emotion, theSemantic Differential (SD) method is one of the most frequently usedfor the purpose. Briefly, adjective pairs of opposite meanings arecreated and subjects rate each of the adjective pairs with a certainnumber of scale so that the SD can convert emotional adjectives intoproper numerical data [Khalid and Helander, 2004; Yang et al.,1999].With this method, the subject’s perception about product forms isquantified on a numerical scale.

2.2. Intelligence powered by people

Early-stage design ideas have a large impact on the cost andquality of a product. During the design process, conceptual design isthe creation of functions to fulfill customer needs, and the creationof forms and behaviors to realize these functions. Designers havethe freedom to generate and explore ideas without being

constrained by parameters that exist at the later design stages. Ifmany ideas are created during conceptual design, there can beplenty of opinions to choose from, and consequently it is morelikely that a good design can be attained. One way to increase thenumber of high quality ideas is to allocate more time for brain-storming [Benami and Jin, 2002; Osborn, 2001]. Compared with theother intuitive techniques that attempt to stimulate human crea-tivity, which include Method 635, Syntactics and C-Sketch, brain-storming is useful for inexperienced individuals [Benami and Jin,2002].

Furthermore, several theorists have argued that group ideasharing like brainstorming should in fact be the source of signifi-cant cognitive stimulation for creativity encouragement [Osborn,2001; Paulus, 2000]. From this perspective, idea exchange orsharing is an important part of group interaction in a variety ofcontexts such as intellectual work group or teams. Accordingly,idea-sharing individuals are exposed to more ideas during theirsessions than solitary idea generators. Thus, there is much potentialfor cognitive stimulation in groups, as long as the group membersattend carefully to the shared ideas [Paulus and Yang, 2000].

From the late 1990s, the word collective intelligence was spreadout by Levy (1997). Collective intelligence is an intelligence thatseemingly has a mind of its own but assembles individuals’ contri-bution together. Therefore, collective intelligence also can refer to assymbiotic intelligence. Today, collective intelligence is emphasizedmore in commercial areas from successful stories of Wikipedia,Amazon, eBay, Web 2.0 and Yahoo! [Lee, 2007]. Atlee (2003) prefersto focus on collective intelligence primarily in humans and activelywork to upgrade the group IQ. For him, collective intelligence can beencouraged to overcome collected intelligence and individualcognitive bias in order to allow a collective to cooperate on oneprocess – while achieving enhanced intellectual performance. Eachindividual solves problems by different mode of thinking. We,however, need to collaborate with the other individuals to createcollective intelligence in order to make a breakthrough for theindividual’s thinking. In this manner, collaboration is the way tomake collective intelligence enhance the creative idea throughdifferent perspectives [Atlee, 2003].

Wisdom of crowds, one of the ideas that branched out fromcollective intelligence, however, does not mean that decision of themajority is always right. Surowiecki who wrote the book ‘‘Wisdom ofCrowds’’ emphasizes the right circumstances required to makea group smarter and the wisdom of crowds can even solve compli-cated problems [Lee, 2007; Surowiecki, 2004]. Table 1 shows thecomparison between collective intelligence and wisdom of crowds.

2.3. Evolutionary computation approach in creativity

In creativity in design, the whole point of the business is tocreate something which other people will experience and which is

Fig. 2. Comparison of GA and IGA process (redrawn from Kim and Cho, 2000).

Table 2Components and factors of a mobile phone.

Components Category of Factors Diagram

Body Soft curvatureSharp-edged rectangleAsymmetricalUnique shape

Receiver Unclosed geometricClosed geometricLinearUnique shape

Display UnbalancedLarge displayFunction keys excludedFunction keys included

Function keys Integrated with displayIsolatedUnique shapeIntegrated with digital keys

Digital keys CircularEllipticalRounded rectangleIntegral

J.-H. Lee, M.-L. Chang / International Journal of Industrial Ergonomics 40 (2010) 295–305 297

in some way or other original and new [Lawson, 2006]. Thus, thedefinition of creativity in design involves the transfer of knowledgefrom other domains, having the ability to generate ‘‘surprising andinnovative solutions.’’ [Gero and Kazakov, 1996] Rosenman (1997)also pointed out that ‘‘the lesser the knowledge about existingrelationships between the requirements and the form to satisfythose requirements, the more a design problem tends towardscreative design.’’

An evolutionary approach can be applied in both the processand the outcome of design [Bentley and Corne, 2002a]. In theprocess of design, evolution can be applied in different stages ofdesign. The evolutionary approach is more consciously, faster, andparadigmatically different then before without evolutionarycomputation [Chang and Lee, 2007b]. According to such advan-tages of evolution for the design generation, evolutionary compu-tation (EC) approach is a modern search technique which usescomputational models of processes of evolution and selection[Kicinger et al., 2005]. There is wide variety of evolutionary algo-rithms exist, with the three main types being: Genetic Algorithm(GA), Evolutionary Programming (EP) and Evolutionary Strategies(ES). EC now provides solutions for applications in creative areassuch as architecture, art, music and design. Some design-aidsystems also have been developed using EC [Janssen et al., 2002;Liu et al., 2004].

Computational creativity approach to evolution not only hasbeen used and explored with creative products in view, but has alsoadded to the computer creator’s armory. Therefore, CESs area promising technique for such enterprise, recently growing favor[Bentley and Corne, 2002b]. The main features that all creativeevolutionary design systems have in common is the ability togenerate entirely new designs starting from little or nothing,guided purely by functional performance criteria. In achieving this,such systems often vary the number of decision variables duringthe evolution process [Rosenman, 1997]. They can often generatesurprising and innovative solutions, or novel solutions qualitativelybetter than others [Bentley, 1999; Harvey and Thompson, 1997].The CES requires some kind of evolutionary algorithm to generatenew solutions. The framework of the CES is as follows: (1) anevolutionary algorithm; (2) a genetic representation; (3) embryo-genesis using components; (4) a phenotype representation; and (5)fitness function(s) and /or processing of user input [Bentley andCorne, 2002a].

Genetic Algorithm (GA) is perhaps the most well known andpopularized of all evolution-based search algorithms and providesa useful commencing framework since they already have a formalrepresentation of various constructs and the combination andmutation operators [Bentley and Corne, 2002a; Gero, 1996]. Theyare a class of algorithms based on the adaptive process of naturalevolution, employing a general uniform knowledge-lean method-ology. They are applied to a natural evolution mechanism likecrossover, mutation, or survival of the fittest for optimization

[Rosenman, 1997]. GAs provide very efficient search methods forworking on population, and have been applied to many problems ofoptimization and classification [Chang and Lee, 2007b].

Interactive genetic algorithms (IGAs) are the same as GAs exceptfor the way of assigning the fitness values of each evolvable indi-vidual. In general, a GA assigns a fitness value to an individual viaevaluating a predefined fitness function. However, formulating thefitness functions of the optimization solutions concerning userpreferences in advance is very difficult. In an IGA, the user givesa fitness value instead of a fitness function to each individual(Fig. 2). In this way the IGA can interact with the user, and can alsoperceive a user’s emotion or preference in the course of theevolution. For this reason IGA can be used to solve problems thatcannot be easily solved by GA, such as design and art domains. Infact, IGA has been reported to have been successfully applied incases of fashion design, industrial design and product design [Wanget al., 2005; Cho, 2002; Takagi, 2001].

As mentioned above, the most important event in the IGA is thefitness assignment issue. The valuable research for the fitnessassignment method has been done by Sugimoto and Yoneyama(2002). They developed a hybrid fitness assignment strategy torealize a natural interaction in IGA. The hybrid strategy allowsa user to select some individuals and evaluate a grade that showshow the selected individual resembles a target image. Gong et al.(2005) proposed a cooperative IGA, which uses the function ofconfidence level to reflect the degree of user’s cognition for anindividual in design process and the preference of an individual’s

Fig. 3. Hierarchical structure of the composition of a mobile phone.

Table 3Image-related adjective pairs for the affective evaluation (from Chuang et al., 2001).

Traditional-modern Heavy-handy Hard-soft Nostalgic-Avant-garde

Large-compact Masculine-Feminine Obedient-Rebellious

Hand-made-Hi-tech

Coarse-delicate Plagiaristic-Creative Rational-Emotional


factor. It is the mechanism to look for the users with same or similarpreference to perform a cooperative evolution.

3. Methodology

3.1. Data collection for the sample of mobile phones andhierarchical structure

In this research, the data relating to mobile phone samples wascollected from current sales and marketing information. The eightmost common and famous brands of mobile phone in Taiwan are:Asus, BenQ-Siemens, LG, Motorola, Nokia, OKWAP, Samsung, andSony Ericsson. Based on these brands, ten types of mobile phonerecently available on the current market in Taiwan were selected.Eighty types of mobile phone were selected and became the datafor the design elements that were used in this research. Thecomponents of a mobile phone were categorized into five parts:body, receiver, display, function keys and digital keys. The factors ofeach component were categorized according to the research resultsfrom Chuang et al. (2001). The category details and factors relatingto the components of a mobile phone are shown in Table 2.

According to the category of the components and factors ofa mobile phone, the hierarchical structure of a mobile phone can beillustrated, as in Fig. 3. The top layer is the component layer, whichinvolves five main components of a mobile phone. Under thecomponent layer is the feature layer. In the feature layer, there aretwo elements, which belong to each component and include thetypes of factors and affection.

3.2. Wisdom of crowds for the affective evaluation of customers’responses

3.2.1. Extracting the affective evaluation using the semanticdifferential method

In this research, the SD method was used to explore customers’affective responses to the design of mobile phones. In the SDmethod, the adjective pairs associated with the phones’ image (the

1 2 3 4

Traditional

extremely quite slightly neu

Fig. 4. An example of the seven-point nu

image adjective pairs) were the main tools used to ask forcustomers’ affective responses. Based on the previous researchfrom Chuang et al. (2001), the eleven affective adjective pairs,which relate to mobile phone, were employed for SD in thisresearch (Table 3). According to the SD method, the seven-pointnumerical scale was used to measure each affective adjective pair;the numerical scale and corresponding meaning is shown in Fig. 4.

3.2.2. Web-based affective analysis engineReal-time information is the major factor that currently impacts

on the competitiveness of the rapid-design environment of mobilephones. In order to achieve the most efficient and flexibleapproaches for getting customers’ affective responses in marketinginstantly, Web-based questionnaire had to be built into thisresearch. This information had to be in a form that could beanalyzed and that could immediately show the customers’ affectiveresponses. The information also had to be in a form that could beshared with the design stakeholder over the Intranet or Extranet,via a standard web browser.

From the SD method via Web, the sequential-scaled numbersare collected and provide quantitative information for each affec-tive adjective. Based on mathematical equations for the SD methodmeasurement from Yang et al. (1999), the SMl

i here can be definedas the average value for each affective image adjective on eachdesign element, as shown in Fig. 5.

3.3. Establishing a computational model for creative generation

Based on the fundamental theories that have been discussed in2.3, the framework of the creativity process can be summarized andillustrated as follows: (1) transferring knowledge form one domaininto a preparation stage; (2) structuring the domain knowledge andincubation into an embryo; (3) observing and gaining an insightinto the embryo; (4) evaluating; and (5) generating solutions.

Accordingly, the concept model of the creative design processinvolved four stages: knowledge stage, activities stage, outcomestage, and judge stage, as shown in Fig. 6. From the knowledgestage, the related knowledge was found via existing or previouscases, and transferred to prepare for the activities stage. Inside theactivities stage, there were several operation modes, including re-associating, restructuring or recombining the design elementsbased on information gained from the insight of knowledge. Thiscould generate possible solutions and evaluations. After suchactivities, the results can be synthesized and new properties orinnovation ideas and solutions can be generated.

5 6 7

Modern

tral slightly quite extremely

merical scale for a pair of adjectives.

Fig. 5. Analysis engine for the evaluation of SD.


According to the model of the creativity design generationprocess, CES plays the role of computation, which supports themechanism (Fig. 7). CES encodes factors into a genetic represen-tation through evolutionary algorithm; the embryogenesissupports the generation mechanism into the restructure stage; thephenotype representation helps the decode function move into thesynthesis stage that produces the possible solution parameters andrepresents the generative idea; and the fitness function providesthe structure for the process of evaluation and judgment.

3.4. Applied interactive genetic algorithm in a hierarchicalgeneration tool

For this research, IGA is particularly suited to enhance thehuman performance during the evolutionary process. IGA can beused to interact with the designers and also understand thedesigners’ subjective conscience or preference in the course of theevolution. However, according to the design case in this research,the mobile phone was composed of several component parts. Dueto this characteristic, the required design solutions could only bereached after a considerable effort had been put into searching forthe designers’ subjective conscience or preferences. In consideringthe efficient ability of searching the evolutionary process, a hierar-chical method was used to separate the global search and the localsearch independent from IGA. Therefore, the composition of mobilephones can be evolved using a global search, and a local search,which can impact on the evolution of each component part. In thisresearch, it was found that by using hierarchical IGA (HIGA), can,not only improve search ability, but also reduce the designs’ fatiguefor the subjective evaluation during the design evolution process.Fig. 8 shows the HIGA process that was applied in this research.

From the beginning, the designer set the affective requirementfor the design element as the fitness function. The system then

Fig. 6. Model of creative des

performed the global search to generate the initial design alter-natives, based on the characteristics of the fitness function. Afterthat, the designer evaluated the initial design alternatives. If thedesigner found the applicable design alternative, the computa-tional mechanism finished. If not, the designer could select a newdesign alternative to crossover or mutate and repeat this processuntil the applicable design alternative was found or until thedesigner decided to perform the local search to re-generate the partconsisting of the element solutions. The process of the local searchwas the same with the global search until the design found theapplicable solution.

Based on the features of the mobile phone, the genotype of theinitial individuals involved the body, receiver, display, function key,and digital key. There were two sub-genotypes to categorizecomponent factors and affective words attached to each individual,as shown in Fig. 9.

According to the genotype setting, the basic elements wereencoded as 46 bit for each chromosome, through binary coding.This involved the: body-type code, receiver-type code, display-typecode, function-key-type code, digital-key-type code, and theaffective code. The detail of each code was described, as shown inTable 4. The structure of the chromosome is shown in Fig. 10.

3.5. Establishing the evaluation share collection (ESC) approach forthe wisdom of crowds

In this research, each designer was able to evolve their ownoptimal population of design solutions, according to their ownsubjective conscience. In order to achieve idea sharing for creativityencouragement during the evolutionary process, the ESC mecha-nism was established to achieve wisdom of crowds during theevolutionary process. The ESC diagram is shown in Fig. 11.

In the ESC, each designer can evolve their own populations ofdesign alternative and submit them to the preference data base,and therefore share the optimum design alternative that reflectstheir own preferences. Each designer can decide whether toperform a collection evaluation, according to their need. Ifa designer decides to perform a collection evaluation, the bestsolution, which is most similar to the current designer, will beselected from the preference database and submitted to the currentdesigner. The current designer can also evaluate the design alter-natives generated by other designers and decide whether to acceptthem. If they do accept the new design alternatives, the worstdesign alternative of the current population will be replaced andthe current designer can evaluate it again and thus find the bestsolution. There are three tasks that must be followed, for the ESCapproach:

ign generation process.

Fig. 7. Model of the creative generation approach.

Fig. 8. The flowchart for the computation mechanism of HIGA.

Fig. 9. Genotype, sub-genotypes and setting of initial individuals.


(1) Apply the hybrid fitness assignment strategy for the evaluationmechanism: For the GA, the fitness function is a measuringmechanism used to evaluate the status of each design alter-native. According to the characteristics of the IGA, the fitnessvalue is taken directly from the human instead of froma computing function. In this research, the hybrid fitnessassignment strategy from Sugimoto and Yoneyama (2002) wasemployed for the fitness assignment mechanism, which allowsa designer to select an optional number from a set of designalternatives in each generation, and evaluate it through

a grading system that shows how the selected design alterna-tive resembles a target image for their preference. Fig. 12 showsthe process of the fitness assignment mechanism.

(2) Establish the confidence mechanism for the preference designsolution: In considering the initial stage of the evolution,a user’s cognition for a design alternative is insufficient and thefitness of a design alternative, evaluated by a designer, is notvery accurate. In order to reduce the effect of a design alter-native’s fitness, which is evaluated by a designer, on theevolution process, the function of ‘‘confidence level’’, from

Table 4Genotypes for the chromosome.

Components Genotype code

Types of factor Affective

00000 TraditionalBody 00 Soft curvature 00001 Modern

01 Sharp-edged rectangle 00011 Heavy10 Asymmetrical 00111 Handy11 Unique shape 01111 Hard

Receiver 00 Unclosed geometric 11111 Soft01 Closed geometric 00010 Nostalgic10 Linear 00110 Avant-garde11 Unique shape 01110 Large

Display 00 Function keys excluded 11110 Compact01 Unbalanced 00100 Masculine10 Large display 01100 Feminine11 Function keys included 11100 Obedient

Function Key 00 Integrated with display 01000 Rebellious01 Isolated 11000 Hand-made10 Unique shape 10000 Hi-tech11 Integrated with digital keys 10111 Coarse

Digital Key 00 Circular 10110 Delicate01 Elliptical 10100 Plagiaristic10 Rounded rectangle 10001 Creative11 Integral 10011 Rational

10101 Emotional

Fig. 11. ESC approach.

Fig. 12. Fitness assignment process.


Gong et al. (2005), is employed to reflect the degree ofa designer’s cognition for a design alternative in the designevolution process. The function of confidence level is shown inequation (1). It is an evolutionary generation, where T is theconfidence level threshold, and a is the confidence coefficient.During the evolution, a designer’s cognition for a designalternative increases and thus the confidence level graduallyincreases. When t reaches T, it can be assumed that a user hascompletely understood the design alternatives, and the pref-erence value of a design alternative’s factor P(h) can obtainedthrough equation (2).

�1þ e�aT � e�aT t < T
RðtÞ ¼1 t � T
(1)

PðhÞ ¼ RðtÞ � fi (2)

(3) Establish the collective mechanism for idea sharing: Whena current designer wants to collect their own past experiencesor collect experiences from other designers, the system willlook for the solutions from past experiences, with the same orsimilar preferences, to perform a cooperative evolution. Anypast experiences with the same preference as the currentdesigner can be found through equations (3) and (4), proposedby Gong et al. (2005). PAðh

jiÞ and PBðhj

iÞ in equation (3) are the

Fig. 10. Chromoso

values of preference from designer A and B for the j-th pref-erence of design factor i respectively. As the designer’s degreeof cognition for all the factors are the same, sðz; kÞ is thendefined as the similarity between current designer z and thedesigner, k, as shown in equation (4).

siðA;BÞ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1Li

XLi

j¼1

�PAðh

jiÞ � PBðhj

iÞ�2

vuut (3)

sðz; kÞ ¼Xn

i¼1

siðz; kÞ (4)

3.6. Modeling of interactive collective generation (ICG) system

The synthesis model embracing all the methods mentionedabove is outlined in Fig. 13. The synthesis model as the paradigm toimplement a proof-of-concept prototype system is called an

me encoding.

Fig. 13. Synthesis model for the prototype implementation.

Fig. 14. Flowchart of ICG system.


Interactive Collective Generation (ICG) system. Fig. 14 illustrates theflowchart and the process of computing in an ICG system.

Fig. 15. System architecture.

4. Prototype implementation

4.1. System architecture

Based on the methodology, which was proposed above, theprototype system implementation was the tool demonstrated inthis paper. The prototype system comprised a questionnairesystem, creative evolutionary system, and an HIGA. The systemarchitecture is shown in Fig. 15. The questionnaire system was usedto retrieve the affective response of the product form of mobilephones from customers. Using the affective analysis engine, itcreated an evolutionary system. In this research, the HIGA was the

Fig. 16. Interface of affective response evaluation of the product form of mobile phones.


main mechanism that connected the designer’s preference data-base with the creative evolutionary system.

4.2. Interfaces and function of prototype

In this paper, there were two system implementations involved.First was the affective evaluation system, the other was the inter-active collective generation system. The interfaces and functionswere shown and described as follows (Figs. 16–19).

4.2.1. Affection evaluation systemIn Fig. 16, the customer can select the brand and type of the

mobile phone they bought. After that, the system will showthe components of the mobile phone, part by part. Accordingly, thecustomer can evaluate the components, depending on their affec-tive response. If the customer confirms the evaluation, the systemcan store the evaluation data into the affective database until thecustomer finishes the evaluation of all the components of themobile phone.

4.2.2. Interactive collective generation systemIn Fig. 17, the current designer can set the condition of the

affective requirements for each design component of the mobile

Fig. 17. Interface of requirement s

phone. After that, they can click on ‘‘generate’’ to trigger the gener-ation function to form the design. In the design alternative area, thesystem will display the design alternatives according tothe requirements setting from the designer, see Fig. 17, (left photo).The current designer can select the design solution from the designalternative area, relying on their subjective conscience or prefer-ence. In this phase, the current designer can evaluate the designsolutions that they select the grading and description, according totheir subjective conscience. After evaluation, the current designercan send the design solution into a population area. In the pop-ulation area, the current designer can click on ‘‘generate’’, to triggerthe generation function. In this phase, the system will perform thegenetic operation and generate the next generation of designalternatives, according to the grading from the current designer anddisplay its design (Fig. 17, right photo). For this evolutionary designprocess, the current designer can operate such functions continu-ously until they find the design solution that can satisfy them.

During the evolutionary design process, the current designeralso can use their own past experiences, or experiences from otherdesigners, to perform collective intelligence. In this way, thecurrent designer can click on ‘‘collect’’ to trigger the collectionmechanism and choosing the collective subject in the pop-upwindow (Fig. 18, left photo). After that, the system can perform the

etting and design generation.

Fig. 18. Interface of collective mechanism.

Fig. 19. Interface of analysis and evolution.


collection mechanism to find the design alternatives from thepreference database, which have the same preference as thecurrent designer from the preference database and then displaythem. For such design alternatives, the current designer can re-evaluate the designs according to their subjective conscience. Afterthe re-evaluation, the design alternatives with high a grading willreplace the design alternatives with a low grading in the populationarea (Fig. 18, right photo). After that, the current designer canoperate the genetic mechanism, continuously (Fig. 19).

If the current designer finds the design solution that satisfiesthem, they can click on ‘‘analysis’’ to analyze the affective conditionin relation to current marketing information. Also, the ‘‘localevolution’’ provides the function to perform the genetic operationin each component.

5. Conclusion

This paper describes a design generation system based oncustomers’ affective responses and is applied to the conceptualdesign of the product forms of a mobile phone. The main intentionof this paper was not only to probe into the communicationmechanisms among customers and designers, but also to shareexperiences with each design stakeholder, which relate to theevolutionary creativity solution.

Here, we applied the HIGA and ESC methods to support theinteractive and collective approaches for design evolvement

support. The research showed that a user-driven approach not onlyimproves the fixed solution from a general generative system, butalso increases individual performance and experience-sharing toenhance creative generation. Not only can the design generationprocess reflect the designers’ subjective conscience through thesame design resource, it also can encourage creativity during thegeneration process.

Moreover, this research shows how the affective evaluationsystem can be used to acquire the customers’ affective responsesrelating to the design of a mobile phone. The benefits derived fromthis design-generation process are that the design stakeholderscan immediately realize their customers’ affective responsesthrough the system and also they can improve the design processby using floating data, based on the daily update of customers’evaluations.

Future research will include: i) extending the work the designer,to increase the hierarchical operation for the components of mobilephones during the design evolution process; ii) increasing in thesystem, the population characteristics for the customer classifica-tion and evaluation mechanisms for the designer’s creation; iii)involving others design factors in the affective effect in designevolvement.

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Stimulating designers' creativity based on a creative evolutionary system and collective intelligence in product design