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

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2017年第1期7卷 1页

作者： Darani, Zahra Sheikhi Kaedi, Marjan Univ Isfahan Fac Comp Engn Hezar Jerib St Esfahan *** Iran

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.

关键词： Interactive design Customer-centric product design Interactive genetic algorithm User fatigue candidate elimination algorithm

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SEED SPACE AND VERSION SPACE - GENERALIZING FROM APPROXIMATIONS

SEED SPACE AND VERSION SPACE - GENERALIZING FROM APPROXIMATI...

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5TH EUROPEAN WORKING SESSION ON LEARNING ( EWSL-91 )

作者： NICOLAS, J INRIA Campus de Beaulieu Rennes cedex 35042 France

ISBN: (纸本)354053816X

This work stands in the continuation of Mitchell’s candidate elimination algorithm (Mitchell 82). Practical applications of this algorithm need the design of a useful “bias”, constraining the size and shape of the Version Space. Our approach strives towards an inclusion of bias at an empirical level. We are working on a conceptual clustering application (Lebbe 89). Our statistical module builds clusters and provides information about attributes involved in the emergence of each cluster, which can be combined to build some approximate generalizations. Thus, we have elaborated on the assumption that we know approximations (the bias) of the concept definitions to be produced. The issue then becomes: 1) How to formalize an approximation in the Version Space prospect? Approximations are represented with a seed set, a set of descriptions comparable with the initial boundary sets for the specificity ordering. The “projection” of the seed set on the version space leads to a focus set. It is an extension of Mitchell’s boundary sets S and G, i.e. a set of consistent descriptions, such that every target definition is “attainable” by at least one of these. 2) How to make profit of these approximations in the C.E.A. prospect? A version space may be viewed as a collection of paths between elements of S and G. The focus set simply restricts the allowed paths by giving intermediate nodes along these paths. The algorithm searches generalizations between two comparable points of the focus set. Since we have no idea of how accurate are the approximations, the algorithm includes a “repair” strategy, loosening the bias until consistency for the whole set of instances. For a detailled report, see (Nicolas 90). © Springer-Verlag Berlin Heidelberg 1991.

关键词： CONCEPT LEARNING GENERALIZATION candidate elimination algorithm BIAS

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A model of integrated learning

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Knowledge-Based Systems 1989年第2期2卷 83-98页

作者： Swaminathan, Kishore Computer and Information Science Department University of Massachusetts Amherts MA 01003 United States

Research in integrated learning is concerned with designing learning systems that combine empirical (inductive) and analytical (deductive) approaches to learning. A model of integrated learning is proposed, and is illustrated with a detailed case study from the field of epidemiology. An algorithm called Negative Instance elimination algorithm is presented. We show that it is orthogonal to the well-known candidate elimination algorithm, i.e. the former is based on a set of assumptions similar to the letter, except that the roles of features and instances are interchanged. © 1989.

关键词： candidate elimination algorithm empirical/analytical/integrated learning Negative Instance elimination algorithm

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