Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs
Sheikhi Darani and Kaedi Hum. Cent. Comput. Inf. Sci.
Improving the interactive genetic algorithm for customer‑centric product design by automatically scoring the unfavorable designs
Zahra Sheikhi Darani
Marjan Kaedi
One of the effective factors in increasing sales is the consistency of products with the preference of the customers. Designing the products consistent with customer needs requires the engagement of customers in the product design process. One way to achieve this goal is the use of interactive evolutionary algorithms. During the running of such algorithms, the customer acts as a fitness function and imparts his/her opinion directly to the design process. Since these algorithms are usually iterated frequently, the user fatigue problem during interaction with them is a major challenge. The present study develops a method to tackle the user fatigue problem in the interactive genetic algorithm using the candidate elimination algorithm. In this method, customer preferences are gradually learned by applying the candidate elimination algorithm on the designs evaluated by the user in the early stages of algorithm. Using the learned preferences, designs which may not meet the customer preferences are discovered and automatically receive a predefined low score from the algorithm. The proposed method has been evaluated on the customer-centric design of book covers and its results have been compared with those of the two simple interactive genetic algorithm and multi-stage interactive genetic algorithm. The results are indicative of a considerable reduction of the number of algorithm generations, the number of chromosomes being evaluated by user, and the evaluating time in comparison with the two aforementioned methods. Reduction of these criteria leads to decrease of user fatigue. In addition, the proposed method has increased the user satisfaction.
Interactive design; Customer-centric product design; Interactive genetic algorithm; User fatigue; Candidate elimination algorithm
Introduction
Appearance of products is one of the factors of customer attraction. This necessitates
customer-centric design of products and product customization [
1–3
]. One of the
methods proposed in this area is the direct use of designs proposed by users for designing
the products. However, offering a design requires specialized knowledge and
necessary skills for working with design software and tools and customers lack such
requirements. Besides, strong presence in competitive markets requires quick development of
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customer-centric designs for products and services. These factors have paved the way
for the emergence of interactive evolutionary algorithms in the design area. Interactive
evolutionary algorithms are a class of problem solving methods in which the human
factor serves as a fitness function [
4
]. One type of interactive evolutionary algorithms is the
interactive genetic algorithm which is explained in “Genetic algorithm and interactive
genetic algorithm” section.
Despite the aforementioned merits, the interaction of humans with evolutionary
algorithms also creates challenges which have been investigated in different studies. One of
the most important of these challenges is user fatigue which is caused by the
evaluation of candidate solutions (where each solution represents a candidate design) in each
algorithm iteration [
4, 5
]. The approach adopted in the present study is prevention of
undesirable solutions using the candidate elimination algorithm in combination with the
interactive genetic algorithm and which reduces the number of generations of the
algorithm, prevents the evaluation of designs which are probably not desirable for the users,
and considerably reduces the time of achievement of a design which is desirable for the
user.
In what follows, first, in “Genetic algorithm and interactive genetic algorithm”
section, the genetic algorithm and the interactive genetic algorithm are briefly discussed.
In “Candidate elimination algorithm” section, the candidate elimination algorithm is
introduced. In “Review of literature” section, the related literature is introduced. In “The
proposed method” and “Evaluation” sections, the proposed method is introduced and
evaluated in comparison with two former methods. Finally, in “Conclusion” section,
some conclusions are drawn.
Genetic algorithm and interactive genetic algorithm
The genetic algorithm, which is one of the best-known evolutionary algorithms, has
been inspired by Darwin’s theory of natural selection [
6
]. In this algorithm, the initia (...truncated)