Estimation of Distribution Algorithms (EDAs) have been recognized as a major paradigm in evolutionary computation. There is no traditional crossover or mutation in EDAs. Instead, they explicitly extract global statistical information from the selected solutions (often called parents) and build a posterior probability distribution model of promising solutions, based on the extracted information....

The use of probabilistic models based on copulas in Estimation of Distribution Algorithms (EDAs) has been identi ed as an emerging research trend on these algorithms for continuous domains. By using copulas, the e ect of the dependence structure and the margins in a joint distribution can be represented separately. Consequently, EDAs based on copulas inherit these characteristics and are able t...

An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs f...

Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs). Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases t...

Evolutionary debunking arguments (EDAs) are arguments that appeal to the evolutionary origins of evaluative beliefs to undermine their justification. This paper aims to clarify the premises and presuppositions of EDAs-a form of argument that is increasingly put to use in normative ethics. I argue that such arguments face serious obstacles. It is often overlooked, for example, that they presuppo...

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...

Evolutionary algorithms (EAs) mimic natural evolution to solve optimization problems. Because EAs do not require detailed assumptions, they can be applied to many real-world problems. In EAs, solution candidates are evolved using genetic operators such as crossover and mutation which are analogs to natural evolution. In recent years, EAs have been considered from the viewpoint of distribution e...

In this chapter we focus on the design of real–valued EDAs for the task of numerical optimization. Here, both the problem variables as well as their encoding are real values. Concordantly, the type of probability distribution to be used for estimation and sampling in the EDA is continuous. In this chapter we indicate the main challenges in this area. Furthermore, we review the existing literatu...

Estimation of distribution algorithms ( EDAs ) is a new kind of evolution algorithm. In EDAs , through the statistics of the information of selected individuals in current group, the probability of the individual distribution in next generation is given and the next generation of group is formed by random sampling. A wide range of mathematical model of the knapsack problem are proposed. In this...

In Lianas and Lantaron, J. Sci. Comput. 46, 485-518 (2011) we proposed an algorithm (EDAS-<i) to approximate the jump discontinuity set of functions defined on subsets of R. This procedure is based on adaptive splitting of the domain of the function guided by the value of an average integral. The above study was limited to the ID and 2D versions of the algorithm. In this paper we address the th...