Decision Support
Using genetic algorithm for dynamic and multiple criteria
web-site optimizations
Arben Asllani
a,1, Alireza Lari
b,*aUniversity of Tennessee, Chattanooga, TN 37403, United States
bDepartment of Management, School of Business and Economics, 1200 Murchison Rd. Fayetteville State University, Fayetteville, NC 28301, United States
Received 25 August 2003; accepted 26 March 2004 Available online 20 January 2006
Abstract
In today’s competitive electronic marketplace, companies try to create long-lasting relations with their online cus-tomers. Log files and registration forms generate millions of online transactions. Companies use new techniques to ‘‘mine’’ these data and establish optimal online storefronts to maximize their web presence. Several criteria, such as minimization of download time, maximization of web-site visualization and product association level, can be used for the optimization of virtual storefronts. This paper introduces a genetic algorithm, to be used in a model-driven deci-sion-support system for web-site optimizations. The algorithm ensures multiple criteria web-site optimizations, and the genetic search provides dynamic and timely solutions independent of the number of objects to be arranged.
2005 Elsevier B.V. All rights reserved.
Keywords: Genetic algorithms; Multiple criteria analysis; Data mining; Web design optimization
1. Introduction
The Internet provides today’s organizations with many competitive capabilities. Through electronic business, firms have opportunities to
increase profitability, reach new markets, improve customer service, distribute products more quickly, and communicate more effectively [9]. Electronic marketing is generating more customer interactivity and is capitalizing on the potential of information technology to transform business pro-cesses [7]. In spite of such potential benefits, few models exist in the marketing literature to exploit the Internet’s unique abilities [1]. In particular, few models address the development of e-business intelligence-that is, the ability to analyze and use 0377-2217/$ - see front matter 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ejor.2004.03.049
* Corresponding author. Tel.: +1 910 672 1249.
E-mail addresses:[email protected](A. Asllani),alari@ uncfsu.edu(A. Lari).
1
Tel.: +1 423 755 4412.
information collected about visitors to an e-busi-ness[23].
Using the click-stream data recorded in web-server log files, Bucklin [4] developed a model of the browsing behavior of visitors to a web site. The author examines two basic aspects of brows-ing behavior: (i) the visitor’s decisions to continue browsing or to exit the site; and (ii) the length of time spent viewing each page. Other models in the literature have aimed to customize the design and content of the e-mail to increase web-site traf-fic. The analysis suggests that the content-targeting approach can potentially increase the expected number of click-throughs by 62%[1]. Other mod-els have emphasized the importance of improving the usability of electronic commerce projects and designing adaptive virtual storefronts that repre-sent customer preferences [2,8,19,27,28,35].
Chickering and Heckerman[5]provide a more structured approach to the maximization of click-through rates, given inventory-management constraints. Their model uses predictive segments in conjunction with a linear program to perform the constrained optimization. Fong and Wong [12]offered another structured model. This model described an online analytical mining of path-tra-versal patterns-thus integrating data-warehouse technology with efficient association mining meth-ods. This research provided architecture to store web-user access paths in a data warehouse[12].
There are two approaches to deciding which types of banners, advertisements, offers, and incen-tives should be presented to web-site visitors: (i) an artistic design; and (ii) an engineering design. Niel-sen [26] emphasized that, apart from aesthetic design and navigational features, the quality and relevance of a web-site’s content play an important role in capturing the short attention span of web surfers. An effective web-site creates an attractive presence that meets several objectives of organiza-tions. These objectives, as presented by Schneider [34], include: (i) attracting visitors to the web-site; (ii) making the site interesting for visitors to stay and explore; (iii) encouraging visitors to follow the site’s links to obtain information and (iv) rein-forcing positive images about the organization that the visitor might already have. Another study suggests a different set of criteria: (i) impression
rate; (ii) monthly cost; (iii) audience fit; (iv) con-tent quality and (v) look and feel[25].
Data-mining tools such as genetic algorithms (GAs) are presently used to recognize patterns, anticipate changes, and learn the buying habits and preferences of electronic commerce customers in Internet-based transactions [6,14,22,33,36,37]. In a similar fashion to that used by physical retail-ers who use data-mining technologies in the design of their stores, web teams can use GAs to assist them in mining for the most effective web-site design for electronic commerce[17,23].
The GA approach was developed in the early 1970s [15,31]. It was based on the mechanism of evolution and has demonstrated its potential for solving intractable optimization problems. An area of great interest is the application of GA in sequenc-ing and deterministic schedulsequenc-ing [10,20,21,30]. There has also been an increasing interest in using GAs as a tool to solve complex combinatorial opti-mization problems [11,13,16,18]. Although GAs are more general and abstract than other optimiza-tion methods, and although they do not always provide the optimal solution, they are considered to be flexible and applicable to a variety of complex environments. Because GAs are best known as an optimization tool, the literature suggests that they can be successfully used only in combination with other data-mining tools. Such a suggestion includes the use of GAs to evaluate the fitness of other tech-niques[14]when coupled with neural networks and machine-learning algorithms[23].
This paper provides a specific GA for the optimal design of a web-site based on multiple opti-mization criteria. This algorithm can assist web-design teams to create adaptive web-sites and to recognize product-line patterns that increase sales, retain customers, and increase visualization. It pro-duces an optimization technique for web-design teams to devise the best arrangements of web-objects in terms of download time, visualization, and potential sales. The optimal ordering of a pre-defined number of web pages is an important objec-tive function for web designers[38].
Finding the best balance between download time and the amount of information offered to an Internet user has also been a primary concern for web designers and developers. Previously
recorded transactions can be used to create a prob-ability matrix. Each element of this matrix repre-sents the likelihood that a given product will be sold if a user shows interest in another product. For example, web-site designers might notice that the offered products on a given web-site are selected on the basis of season, price, and promo-tion. However, once the customer purchases (or shows interest in) a given product, a set of associ-ated products or services can be suggested in the links.
2. Concepts of the proposed system
The conceptual design of the GA presented in this paper is based on the guidelines provided by Houpt and Houpt [16]. Specific details of this GA are based on the work of others. For example, the combination of two optimization functions into a single fitness value is based on the work of Neppalli et al.[24], the incorporation of setups is based on the work of Rubin and Ragatz [32], and crossover operator design is based on Poon and Carter[29].
This study considers download time, visualiza-tion and product associavisualiza-tion level as major deter-minants of an effective web-site. Before describing the model, several components of the problem def-inition require clarification.
Web-object: In this paper, the term
‘‘web-object’’ refers to any component in the web-site that is used to describe or advertise a product or service. Examples of web-objects include banners, images, splash screens, leased spots, sounds, and other multimedia objects.
Download time: Practitioners and researchers
have identified the waiting time as a major prob-lem that must be resolved in order for people to have rich robust Internet experiences. For the fore-seeable future, slow download speeds may cost web-sites billions of dollars in opportunity loss [38]. Most customers connect to the Internet via a modem. If web-objects or files are kept small, users with slower connections are able to access the files within a reasonable time. Download time depends on the file size and connection speed. Web designers are constantly searching for ways to
improve their works. Recently published books provide such recommendations, but their quality varies greatly. How usability testing was used to validate design recommendations is described. The results show a need for navigational aids that are related to the particular web-site and located beneath the Browser buttons. Furthermore, usability criteria were established that limit page changes to 4 and search times to 60 seconds for information retrieval [3].
Visualization: Visualization is the process
whereby complex data sets are transformed into meaningful visual images. Images differ from each other in terms of colors, size, speed, curvature, and so on. In this paper, visualization is represented by assigning a number from 1 to 10, where 1 represents minimal visualization (usually small paragraphs of plain text) and 10 represents optimal visualization (a bright and colorful splashing image).
Increasing potential sales: The probability of
selling a product or service represented by a given web-object (B) when another web-object (A) is vis-ited can be expressed in the following terms:
PAB¼
NSBA
NVA
; ð1Þ
where
PAB the probability that the product or service presented by web object B is sold after web-object A is visited;
NSBA the number of times the product or service
presented by web object B is sold after web-object A is visited during a given per-iod of time;
NVA the total number of times web-object A is
visited during a given period of time. The structure of the proposed algorithm is shown inFig. 1.
In GA, a generation of individuals consists of the surviving individuals from the previous gener-ation together with new solutions or offspring. The population size usually remains constant from one generation to the next. The offspring are generated through reproduction and mutation of individuals (parents) from the previous generation. A muta-tion in a parent chromosome might be equivalent
to a pair-wise interchange in the corresponding sequence. In each generation, the fittest individuals reproduce and the least-fit ones die. As shown in Fig. 1, the proposed GA starts by defining the main parameters. Table 1 presents and describes
the main parameters that affect the efficiency of the algorithm.
Step 1: Generate initial population
Each web-object consists of several members— such as product name, download time, visualiza-Fig. 1. Genetic algorithm for multiple criteria web optimization.
tion score, and likelihood that the product will be sold in combination with other products or ser-vices. Each sequence of web-objects consists of two members: (i) anN-dimensional array of web object structures (the sequence of the objects vis-ited by users); and (ii) an N·N probability matrix. Each element of this matrix represents the probability that the product or service repre-sented by web-object j will be sold if it appears after web-object i in the sequence. Whenever a new sequence is created, the probability matrix must be adjusted accordingly. This is handled by creating a specificadjust-probabilityfunction. This function uses the list of web objects in a given sequence as a parameter and is called upon every time a crossover or a permutation occurs. Each generation consists of a vector of sequences of POPSIZE—the population size. For the first gen-eration, applying a mutation operator to the initial sequence creates the initial population. Subsequent generations are created as described in Step 3 of the algorithm.
Step 2: Evaluate fitness value
The fitness value is used as an optimization objective function for the algorithm. The values for total download time, visualization score, and
probabilities for each sequence are normalized, redirected, and then factored with respective weights. The fitness value can be calculated as follows: F ¼ Pm k¼1DðkÞ Maxm k¼1DðkÞ m ! w1 þ 1 Pm k¼1VðkÞ Maxm k¼1VðkÞ m ! w2 þ 1 Pm k¼1PðkÞ Maxmk¼1PðkÞ m ! w3; ð2Þ where
D(k) download time of the kth web-object in the sequence;
V(k) visualization score of the kth web-object in the sequence;
P(k) probability that the product or service represented by the kth web-object will be sold if followed by the (k1)th web-object in the sequence;
Table 1
Main parameters of GA for web optimization
Parameter Notation Description Variable
Number of candidate web-objects
N Number of web-objects to
be considered for sequencing
Independent Number of web-objects M Number of web-objects to be
sequenced
Independent Population size POPSIZE Number of sequences in each
generation
Independent
Weights w1,w2,w3;w3=
1(w1+w2)
Values between 0 and 1 used to ponder three objective functions into the fitness value function
Independent
Mutation rate MUTRATE A value between 0 and 1, which represents the portion of new members generated by a mutation
Independent
Crossover rate CROSRATE A value between 0 and 1, which represents the portion of new members generated by crossover
Independent
Mutation vs crossover MUTCROS Ratio between mutation and crossover rate
Independent Number of generations GEN Number of generations needed to
achieve an optimal solution
w1,w2,w3 respective weights assigned to each component of the fitness value; and
m number of web-objects in the sequence. Fitness function F is applied to only m web-objects, a subset of ncandidate web-objects to be arranged in the web-site. As such, the algorithm serves not only as an optimization, but also as a pat-tern-recognition tool. Among all possible products and services to be offered, the algorithm identifies only a small group of these products and services to be displayed in a given web-site—that is, those that minimize the multi-criteria fitness function.
Step 3: Create new generation
After sorting the population members of the previous generation in an ascending order based on the fitness value, the process of creating a new generation consists of three main steps:
1. Keep best members:The process of assigning
the first few best members from the old generation to the new generation ensures a gradual improve-ment of the solution. The algorithm also saves the best sequence as a candidate optimal solution.
2. Crossover operator:The following crossover
operator code is suggested. This operator gener-ates new sequences and ensures the feasibility of the algorithm.
• Step a:Select sequences PARENT 1 and
PAR-ENT 2 as two sequences from the old generation.
• Step b: Generate k as a random number
between 0 and N, where N is the number of web-objects in the sequence.
• Step c:Select the firstkmembers of PARENT 1
and save them in the new OFFSPRING.
• Step d:Complete the remaining (N–k) members of the OFFSPRING by following this rule: If the remaining members from PARENT 1 appear in the MOTHER sequence, add the appearing members to the OFFSPRING fol-lowing the same order in which they appear in the PARENT 2 sequence.
• Step e: Adjust the probability matrix for the
OFFSPRING sequence.
3. Mutation operator:The crossover operator is
focused on creating alternative solutions around the best solutions achieved thus far. To avoid the
risk of remaining in local optima, a mutation oper-ator is used. For sequencing problems, mutation can be achieved by swapping two random web-objects in a given sequence. The algorithm for this process consists of the following steps:
• Step a:Randomly generate two integerskands
between 0 and N, where N is the number of web-objects in the sequence.
• Step b:Swap web-objects that are in thekth and
sth position in a given SEQUENCE.
• Step c: Adjust the probability matrix for the
new SEQUENCE.
The process of creating new generations contin-ues until a given number of generations is achieved or the fitness value of a given solution achieves an acceptable level.
3. Example
Suppose that company E sells a total of 10 products via its web-site. Each of these products is represented by a web-object—namely a–j.Table 2 shows the past records of Internet-based sales and customer logs. This matrix is periodically and dynamically updated on the basis of records from customer logs.
The following GA steps are based inFig. 1.
Start: Define parameters:
• number of candidate web-objects: 10
• number of appearing web-objects: 5. It is assumed that first 5 objects are selected in the first iteration—that is, a–e
• population size: 10 members
• number of generations: 100 (in this example only one generation is shown)
• mutation rate: 30% of new members of next generation
• crossover rate: 20% of new members of next generation
• weights: assumed equal in this example
Step 1: Generate initial population
As noted above, the following 5 out of 10 objects are selected as the initial web-object
struc-ture. The initial population and the probability matrix are shown inTable 3.
Next, the new members are mutated. By muta-tion (switching any two members and re-adjusting
Pij matrix), 10 new members are created
(popula-tion size is 10). For each matrix, the adjust-matrix function is used. For example,Pijfor member M1
is shown in Table 4.
Step 2: Evaluation of fitness function
From Table 2, MAX DT = 20, MAX VC = 5, and MAX Pij= 0.6. The evaluation fitness
func-tion is done inTable 5.
In this case, the weights for each component of the objective function have been considered equal (w1=w2=w3).
Fitness value for
FM1 = 32/100a+ (113/25) + (1.4/3) = .32 + .48 + .8 = 1.6 FM2 = 1.73 FM3 = 1.53 FM4 = 1.6 FM5 = 1.76 FM6 = 1.6 FM7 = 1.6 FM8 = 1.53 FM9 = 1.46 FM10 = 1.53 a
This 100 is from multiplying MAX DT (20) by m(5). Table 3
The probability matrix for initial population
a b c d e DT 10 5 5 10 2 VC 4 2 1 5 1 Pij a .1 0 0 .2 .2 b .4 .4 0 0 0 c .3 .3 .3 .1 0 d 0 0 0 .1 0 e .1 .3 .2 .1 0 Table 4 Pijmatrix for M1 a d c b e DT 10 10 5 5 2 VC 4 5 1 2 1 Pij a .1 .2 0 0 .2 d 0 .1 0 0 0 c .3 .1 .3 .3 0 b .4 0 0 .4 0 e .1 .1 .2 .3 0 Table 2
Initial data for illustrative example
Web-object a b c d e f g h I j
Download time in seconds with a 56kmodem connectiona(DT)
10 5 5 10 2 7 15 20 7 12
Visualization coefficientb(VC) 4 2 1 5 1 3 5 5 3 4
Probability of selling productjwhen iis visited immediately before (customer visits objectiand buys objectj);c
Piimeans productiis sold without a
prior visit to another product
a .1 0 0 .2 .2 .1 .2 .1 .1 0 b .4 .4 0 0 0 0 0 .2 0 0 c .3 .3 .3 .1 0 0 0 0 0 0 d 0 0 0 .1 0 .6 0 .3 0 0 e .1 .3 .2 .1 0 .3 0 0 0 0 f 0 0 0 .5 0 0 0 0 .3 .2 g 0 0 0 0 0 .5 0 .2 .3 0 h .1 .1 .1 .2 .1 .1 0 0 .1 .2 i .1 0 .4 0 .5 0 0 0 0 0 j .1 .1 .3 0 0 .4 0 .05 .05 0
a Time is proportional with web-object size.
b For example, 1 is used for Text, 2 for Bold text, 3 for B&W image, 4 for color, and 5 for splash image, etc.
c This probability will change whenever the sequence of objects change based on customer logs from past transactions. This is a dynamic matrix.
Step 3: Create new generation
(i)Find the best member
The lowest is M9 with the order of d, e, c, b, a. It is saved as the best sequence so far. Keeping this solution ensures both feasibility and no deterioration of the solution in future genera-tions.
(ii)Crossover operation:
(a) Randomly select two members such as Prod-uct 1 (M3: c a d b e) and ProdProd-uct 2 (M6: a e b d e).
(b) A random number between 1 and 5 is selected (k= 2).
(c) Select the first two members of Product 1 (c a d b e) and save them in new OFFSPRING. (d) Complete OFFSPRING.
Because the crossover rate is 20%, two (out of ten) offspring are created via crossover. Suppose M2 = a c b d e (in new generation).
Step 4: Mutation
(i) Generate a random number ‘‘K’’ between 0 and 5 (for example, k= 4) and a random number ‘‘S’’ between 0 and 5 (for example,
s= 5).
(ii) Swap web-objects that are in the 4th and 5th positions of M4 to generate a new member. The M3 (of new generation) is = MUTATE (M4 of old generation) in which the place of ‘‘e’’ and ‘‘d’’ are switched. In this way, with a mutation rate of 30%, three new members are created.
Best solution so far: (d e c b a). Five new members of next generation:
M1 {c a e b d} and M2 {a c b d e} by crossover. M3 {c a b e d}, M4 {b a c e d}, and M5 {d a c e b} by mutation.
Because the population size is 10, 5 more mem-bers are added by randomly selecting 5 more sequences of five web-objects each from the pool of total objects (10) available as shown in Table 1. This generation is completed as follows (randomly): M6 {a b e f g} M7 {i h a c d} M8 {h i c a d} M9 {a b d f g} M10 {i g f a c} Sort the data in ascending order and select the
lowest value and keep
M9 1.46 M3 1.53 M8 1.53 M10 1.53 M4 1.6 M6 1.6 M7 1.6 M1 1.66 M2 1.73 M5 1.76 M1 in new generation c a e b d info 5 10 2 5 10 DT 1 4 1 2 5 VC c .3 .3 0 .3 .1 a 0 .1 .2 0 .2 e .2 .1 0 .3 .1 b 0 .4 0 .4 0 d 0 0 0 0 .1 Table 5
Fitness function evaluation
Sequence RDT RVC RPij M1 adcbe 32 13 .1 + .2 + 0 + .3 + 0 = .6 M2 acdbe 32 13 .1 + 0 + .1 + 0 + 0 = .2 M3 cadbe 32 13 .3 + .3 + .2 + 0 + 0 = .8 M4 cabde 32 13 .3 + .3 + 0 + 0 + 0 = .6 M5 eabdc 32 13 0 + .1 + 0 + 0 + 0 = .1 M6 aebdc 32 13 .1 + .2 + .3 + 0 + 0 = .6 M7 aecdb 32 13 .1 + .2 + .2 + .1 + 0 = .6 M8 aecbd 32 13 .1 + .2 + .2 + .3 + 0 = .8 M9 decba 32 13 .1 + 0 + .2 + .3 + .4 = 1 M10 edcba 32 13 0 + .1 + 0 + .3 + .4 = .8
There is a loop here. At this time M1, M2,. . ., M10 are used as the new generation and the fitness value is calculated as shown above. Again, MAX DT, VC, andPij do not change (they are selected
from the same pool of data). The best solution of this generation is composed of the best solution of the previous generation. If the new solution is worse, the old one is kept. Iteration continues until the number of generations is completed (100 in the present assumptions) or until no improvements are noticed.
To measure the performance of the algorithm, 80 web-optimization problems were generated. Regression analysis was performed (considering the number of generations). (GEN) was taken to be the dependent variable, while the number of can-didate web-objects (N), the number of appearing web-objects (M), the population size (POPSIZE), mutation rate (MUTRATE), and crossover rate (CROSRATE) were taken to be independent vari-ables.Table 6shows the results of this analysis.
Since there is no indication of a correlation between any of the independent variables with the dependent variable, it can be concluded that the proposed algorithm performs well in spite of the complexity and other input factors.
4. Conclusions
Since the Internet was introduced in 1969, it has evolved to become a mainstream channel of com-munication and has been rapidly gaining popular-ity as a medium for electronic commerce. The rapid growth of the Internet has presented a host
of new opportunities to business. In the mid-to-late 1990s, it was apparent that the trend to Business-to-Business automation would have a profound impact on supply chain performance. Between 1998 and 2000, hundreds of e-markets were established in dozens of industries. Among other things, these marketplaces promised increased market reach for buyers and suppliers, reduced procurement costs, and paperless transac-tions. With the rapid diffusion of the Internet, marketing in cyberspace is fast becoming an alter-native channel for production of marketing services.
The emergence of e-marketing has prompted many organizations to rethink their IT strategies in order to stay competitive. Customers today are demanding much from marketing services. They want new levels of convenience and flexibility on top of powerful and easy-to-use marketing-management tools.
Through e-marketing, firms have the opportu-nity to reach new markets, improve customer ser-vice, and increase profitability. Web-sites are important tools for e-marketing. The design of adaptive virtual storefronts that represent cus-tomer performances is very important. An effective web-site creates an attractive presence that meets several objectives. These objectives include attract-ing visitors to the web site, makattract-ing the site interest-ing for visitors, and reinforcinterest-ing positive images about the organization.
The present research has provided a structured approach to web-site design for web designers who are constantly searching for ways to improve their works. By providing a technique Table 6
Coefficients of regression
Model Unstandardized coefficients Standardized coefficients t Sig.
B Std. error Beta 1 (Constant) 556.781 197.575 2.818 .006 # of candidate web-objects 5.321 4.974 .198 1.070 .288 Population size .603 1.476 .055 .409 .684 Mutation rate 238.221 213.775 .147 1.114 .269 Crossover rate 154.254 192.662 .106 .801 .426 # of appearing web-objects 8.624 7.716 .182 1.118 .267
for multiple-criteria web-design optimization using a genetic algorithm, the approach presented here can assist them to capitalize on the benefits of elec-tronic commerce systems. This technique provides a solution for the best-possible arrangements of a given set of web-objects based on simultaneous multiple criteria: (i) download time; (ii) visualiza-tion; and (iii) product association level. Such crite-ria are consistent with both aesthetic design principles and with the quality and relevance of content offered. The algorithm can be extended to include additional criteria and can play an important role in improving the overall perfor-mance of a given web-site.
The GA approach provides good solutions regardless of the number of web objects to be arranged. GA can also be easily incorporated as a ‘hidden’ optimization program in a web-site. A C++ program has been designed on the basis of this algorithm, and statistical analysis has shown that the algorithm performs well-regardless of the number of web-objects to be considered for optimization.
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