@MASTERSTHESIS{ 2017:1480652622,
 	title = {Estimation of distribution algorithms for clustering and classification},
 	year = {2017},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/7384",
 	abstract = "Extracting meaningful information from data is not an easy task. Data can come in batches or through
 a continuous stream, and can be incomplete or complete, duplicated, or noisy. Moreover, there are
 several algorithms to perform data mining tasks, and the no-free lunch theorem states that there
 is not a single best algorithm for all problems. As a final obstacle, algorithms usually require hyperparameters
 to be set in order to operate, which not surprisingly often demand a minimum knowledge
 of the application domain to be fine-tuned. Since many traditional data mining algorithms employ a
 greedy local search strategy, fine-tuning is a crucial step towards achieving better predictive models.
 On the other hand, Estimation of Distribution Algorithms perform a global search, which often is
 more efficient than performing a wide search through the set of possible parameters. By using a
 quality function, estimation of distribution algorithms will iteratively seek better solutions throughout
 its evolutionary process. Based on the benefits that estimation of distribution algorithms may offer
 to clustering and decision tree-induction, two data mining tasks considered to be NP-hard and NPhard/
 complete, respectively, this works aims at developing novel algorithms in order to obtain better
 results than traditional, greedy algorithms and baseline evolutionary approaches.",
 	publisher = {Pontifícia Universidade Católica do Rio Grande do Sul},
 	scholl = {Programa de Pós-Graduação em Ciência da Computação},
 	note = {Faculdade de Informática}
}