Fashion Design Using Interactive Genetic Algorithm with Knowledge-based Encoding

2005 ◽  
pp. 411-434 ◽  
Author(s):  
Hee-Su Kim ◽  
Sung-Bae Cho
Keyword(s):  
Genetic Algorithm ◽  
Fashion Design ◽  
Interactive Genetic Algorithm ◽  
Knowledge Based

Interactive Sketch Design Recognition System Using Evolutionary Techniques

2014 ◽  
Vol 18 (1) ◽  
pp. 89-103
Author(s):  
P. Y. Mok ◽  
X.X. Wang ◽  
J. Xu ◽  
J.T. Fan ◽  
Y.L. Kwok ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Subjective Evaluation ◽  
Recognition System ◽  
Experimental Results ◽  
Fashion Design ◽  
Recognition Method ◽  
Sketch Recognition ◽  
Interactive Genetic Algorithm ◽  
Recognition Process ◽  
Composite Description

In this study, a methodology integrating a sketch design recognition approach with an interactive genetic algorithm is proposed to help laypersons get clothes reflecting their preferences. The sketch design recognition approach consists of a composite description model, a sketch recognition method and a database. First, a composite description model based on the knowledge of fashion design is developed to describe the characteristics of a skirt. Second, a sketch recognition method is used to help laypersons get satisfactory clothes. Third, a database in constructed to record general elements of skirts. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch recognition process. The experimental results demonstrate that the proposed method can 'recognize' users' preferred styles efficiently. The subjective evaluation has shown that the system can help general consumers without any fashion design or sketch training to create their own designs.

Optimization of Preloaded Coefficient Based on the Adapted CBR

2009 ◽  
Vol 628-629 ◽  
pp. 13-18
Author(s):  
H.L. Li ◽  
Li Hui Lang ◽  
W. Jiao ◽  
H.Z. Su
Keyword(s):  
Genetic Algorithm ◽  
Optimum Design ◽  
Prediction Error ◽  
Selection Model ◽  
Knowledge Based Systems ◽  
Estimation Model ◽  
Knowledge Based ◽  
Adjustment Model ◽  
Successful Technique

Selecting an appropriate preloaded coefficient has always been a challenge in wire- winding prestressed structure optimum design. Cased-based reasoning (CBR) has become a successful technique for knowledge-based systems in many domains. However, hardly any research has addressed the issue of how to generate the adaptation solution when the case has been retrieved. The present paper investigates the adoption of genetic algorithm(GA) to explore the suitable adjustment model. Two adapted model were presented and assessed in terms of their mean relative prediction error rates.The experiment results shown that applying GA to adjust the preloaded coefficient selection model is a feasible approach to largely improve the accuracy of estimation model. It also demonstrate that the adapted CBR presents better estimate accuracy than the results ontained by other unadapted CBR methods.

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