TY  - JOUR
AB  - Purpose– The purpose of this paper is to develop an intelligent system for fashion style selection for non-standard female body shapes. Design/methodology/approach– With the goal of creating natural aesthetic relationship between the body shape and the shape of clothing, garments designed for the upper and lower body are combined to fit different female body shapes, which are classified as V, A, H and O-shapes. The proposed intelligent system combines genetic algorithm (GA) with a neural network classifier, which is trained using the particle swarm optimization (PSO). The former, called genetic search, is used to find the optimal design parameters corresponding to a best fit for the desired target, while the task of the latter, called neural classification, is to evaluate fitness (goodness) of each evolved new fashion style. Findings– The experimental results are fashion styling recommendations for the four female body shapes, drawn from 260 possible combinations, based on variations from 15 attributes. These results are considered to be a strong indication of the potential benefits of the application of intelligent systems to fashion styling. Originality/value– The proposed intelligent system combines the effective searching capabilities of two approaches. The first approach uses the GA for identifying best fits to the target shape of the body in the solution space. The second is the PSO for finding optimal (with respect to training mean-squared error) weight and threshold parameters of the neural classifier, which is able to evaluate the fitness of successively evolved fashion styles.
VL  - 27
IS  - 2
SN  - 0955-6222
DO  - 10.1108/IJCST-02-2014-0022
UR  - https://doi.org/10.1108/IJCST-02-2014-0022
AU  - Vuruskan Arzu
AU  - Ince Turker
AU  - Bulgun Ender
AU  - Guzelis Cuneyt
PY  - 2015
Y1  - 2015/01/01
TI  - Intelligent fashion styling using genetic search and neural classification
T2  - International Journal of Clothing Science and Technology
PB  - Emerald Group Publishing Limited
SP  - 283
EP  - 301
Y2  - 2024/09/19
ER  -