@article {30,
	title = {Optimal quantization : evolutionary algorithm vs stochastic gradient},
	journal = {Proceedings of the 9th Joint Conference on Information Sciences},
	year = {2006},
	publisher = {Atlantic Press},
	abstract = {We propose a new method based on evolutionary optimization for obtaining an optimal $L^p$-quantizer of a multidimensional random variable. First, we remind briefly the main results about quantization. Then, we present the classical gradient-based approach (this approach is well detailed in [2] and [7] for $p=2$) used up to now to find a {\textquotedblleft}local{\textquotedblright} optimal $L^p$-quantizer. Then, we give an algorithm that permits to deal with the problem in the evolutionary optimization framework and illustrate a numerical comparison between the proposed method and the stochastic gradient method. Finally, a numerical application to option pricing in finance is provided.},
	attachments = {http://www.quantize.maths-fi.com/sites/default/files/Evolutionary-Algorithms.pdf},
	author = {Moez Mrad and Sana Ben Hamida}
}
