TY  - JOUR
A1  - Renault, Manon
ED  - Bièvre-Perrin, Fabien
ED  - Carlà-Uhink, Filippo
ED  - Rollinger, Christian
ED  - Walde, Christine
T1  - Antiquités et pop cultures dans la haute couture et le prêt-à-porter des années 2010
JF  - thersites 13: Antiquipop – Chefs d’œuvres revisités
N2  - From the fluid dresses woven from precious materials evoking the iconic statues of Antiquity to the revival of Spartan shoes, two emblematic fashion trends will help us study the place of Greek Antiquity in contemporary women’s fashion collections. Ordinary as well as extraordinary, what do these reminiscences tell? Can they permit to understand the boundaries that structure and govern the fashion’s worlds? Numerous and diverse, the differences and the similarities of the ways in which classical references are used allow us to study the relations of power in which the specificities of haute couture and ready-to-wear are defined. The values, the entry criteria, the operating hierarchies as well as the very acceptance of the word “fashion” are different from one environment to another. From the catwalks of big fashion houses on Avenue Montaigne such as Chanel to the youngest brands, the differentiated readings and uses of Antiquity raise the question of the symbolic value of classics in fashion.
KW  - fashion
KW  - antiquity
KW  - sociology
KW  - Fashion Studies
KW  - pop culture
Y1  - 2021
U6  - https://doi.org/10.34679/thersites.vol13.149
VL  - 2021
IS  - 13
SP  - 125
EP  - 140
ER  - 
TY  - JOUR
A1  - Doerr, Benjamin
A1  - Krejca, Martin S.
T1  - Significance-based estimation-of-distribution algorithms
JF  - IEEE transactions on evolutionary computation
N2  - Estimation-of-distribution algorithms (EDAs) are randomized search heuristics that create a probabilistic model of the solution space, which is updated iteratively, based on the quality of the solutions sampled according to the model. As previous works show, this iteration-based perspective can lead to erratic updates of the model, in particular, to bit-frequencies approaching a random boundary value. In order to overcome this problem, we propose a new EDA based on the classic compact genetic algorithm (cGA) that takes into account a longer history of samples and updates its model only with respect to information which it classifies as statistically significant. We prove that this significance-based cGA (sig-cGA) optimizes the commonly regarded benchmark functions OneMax (OM), LeadingOnes, and BinVal all in quasilinear time, a result shown for no other EDA or evolutionary algorithm so far. For the recently proposed stable compact genetic algorithm-an EDA that tries to prevent erratic model updates by imposing a bias to the uniformly distributed model-we prove that it optimizes OM only in a time exponential in its hypothetical population size. Similarly, we show that the convex search algorithm cannot optimize OM in polynomial time.
KW  - heuristic algorithms
KW  - sociology
KW  - statistics
KW  - history
KW  - probabilistic
KW  - logic
KW  - benchmark testing
KW  - genetic algorithms
KW  - estimation-of-distribution
KW  - algorithm (EDA)
KW  - run time analysis
KW  - theory
Y1  - 2020
U6  - https://doi.org/10.1109/TEVC.2019.2956633
SN  - 1089-778X
SN  - 1941-0026
VL  - 24
IS  - 6
SP  - 1025
EP  - 1034
PB  - Institute of Electrical and Electronics Engineers
CY  - New York, NY
ER  -