Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs

Human-centric Computing and Information Sciences, Oct 2017

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.

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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 © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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)


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Zahra Sheikhi Darani, Marjan Kaedi. Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs, Human-centric Computing and Information Sciences, 2017, pp. 38, Volume 7, Issue 1, DOI: 10.1186/s13673-017-0119-0