اخیراً داده‌ها در سازمان‌ها به دارایی ارزشمندی تبدیل شده‌اند و حاکمیت داده یکی از اولویت‌های شده است. بررسی مطالعات پیشین نشان می‌دهد که سنجش استقرار به‌صورت کیفی انجام می‌شود نمی‌توانند بر اساس این نوع برنامه‌ای را برای بهبود وضعیت خود تعیین کنند. هدف پژوهش، ارائه روشی کمّی سطح یک سازمان متعاقباً برنامه‌ریزی موجود با توجه ماهیت عوامل تأثیرگذار میزان مفاهیم فازی مدل‌سازی تحلیل استفاده است؛ همچنین ...

The purpose of this paper is to establish some guidelines for designing effective Estimation of Distribution Algorithms (EDAs). These guidelines aim at balancing intensification and diversification in EDAs. Most EDAs are able to maintain some important linkages among variables. This advantage, however, may lead to the premature convergence of EDAs since the probabilistic models no longer genera...

Research into the dynamics of Genetic Algorithms (GAs) has led to the field of Estimation–of–Distribution Algorithms (EDAs). For discrete search spaces, EDAs have been developed that have obtained very promising results on a wide variety of problems. In this paper we investigate the conditions under which the adaptation of this technique to continuous search spaces fails to perform optimization...

BACKGROUND AND PURPOSE Encephaloduroarteriosynangiosis (EDAS) has become the main treatment for moyamoya disease, a chronically progressive cerebrovascular occlusive disease in children. We aimed to assess the utility of perfusion-weighted MR imaging for evaluating hemodynamic changes before and after EDAS. METHODS Thirteen patients with angiographically confirmed moyamoya disease who underwe...

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. This explicit use of probablistic models in optimization offers some significant advantages over other types of metaheuristics. This paper discusses these advantages and outlines...

The use of probabilistic models based on copulas in EDAs (Estimation of Distribution Algorithms) is currently an active area of research. In this context, the copulaedas package for R intends to provide a platform where EDAs based on copulas can be implemented and studied. The package offers complete implementations of various EDAs based on copulas and vines, a group of well-known benchmark pro...

Learning models from data which have the double ability of being predictive and descriptive at the same time is currently one of the major goals of machine learning and data mining. Linguistic (or descriptive) fuzzy rule-based systems possess a good tradeoff between the aforementioned features and thus have received increasing attention in the last few years. In this chapter we propose the use ...

Estimation of distribution algorithms (EDAs) guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This i...