Estimation of Distribution Algorithm (EDA) is a relatively new optimization method in the field of evolutionary algorithm. EDAs use probabilistic models to learn properties of the problem to solve from promising solutions and use them to guide the search process. These models can also reveal some unknown regularity patterns in search space. These algorithms have been used for solving some chall...

Multivariate Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs), and covariance matrix plays the essential role in guiding the evolution. In this paper, we propose a new framework for Multivariate Gaussian based EDAs (MGEDAs), named Eigen Decomposition EDA (ED-EDA). Unlike classical EDAs, ED-EDA focuses on eigen analysis of the covariance matrix, and i...

In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to s...

The extension of estimation of distribution algorithms (EDAs) to the multi–objective domain has led to multi–objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi–objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding “standard”...

Estimation of Distribution Algorithms (EDAs) combine genetic algorithms with statistical modeling in order to learn and exploit the structure of search domains. Such algorithms work well when the EDA’s statistical model matches the structure of the domain. Many EDAs use statistical models that represent domain structure with directed acyclic graphs (DAGs). While useful in many areas, DAGs have ...

The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analy...

Evolutionary algorithms (EAs) have become one of the successful optimization methods during the last few decades, especially for problems where smooth optimization cannot be used. This work summarises present trends in Estimation of distribution algorithms (EDAs), a recent kind of EAs, and suggests a further research direction. These algorithms evolve a population of partial solutions, and inst...

In Internet of Things (IoT), the main driving technologies are considered to be tiny sensory objects. These objects cannot host traditional preventive and detective technologies to provide protection against the increasing threat sophistication. Furthermore, these solutions are limited to analyzing particular contextual information, for instance network information or files, and do not provide ...

Optimization problems occur widely in various domains and are of great practical importance. Better solutions to optimization problems translate into increased production or efficiency, or less waste or error. As a result, the development of optimization algorithms is a large and active area of research. Although there is a large amount of theory and techniques for solving optimization problems...