Estimation of Distribution Algorithms (EDA) are population-based meta-heuristics that use probabilistic models to describe the relationships between variables of a problem. From these models, new solutions can be sampled without disruption of Building Blocks, leading to a more effective search. The key aspect of EDAs is the model-building step and better models should enable a better exploratio...

This paper introduces new analytical concepts to reveal overlooked dimensions of power inequalities between elite development agents (EDAs), local and the targets aid. Affective privilege captures positioning EDAs within affective patterning that sustains their dominant position. They enjoy a greater capacity affect others in ways reproduce structural power. resilience reduced be affected chall...

A very general class of EDAs is defined, on which universal results on the rate of diversity loss can be derived. This EDA class, denoted SML-EDA, requires two restrictions: 1) in each generation, the new probability model is build using only data sampled from the current probability model; and 2) maximum likelihood is used to set model parameters. This class is very general; it includes simple...

Estimation of Distribution Algorithm (EDA) is a new concept in the field of evolutionary computation, which is motivated by the idea of building a probabilistic model of the population to prevent the disruption of building blocks in subsequent generations. It throws out the genetics of evolutionary computation; instead, produces solutions by using techniques developed in statistics, artificial ...

In the real-world problems, we are likely confronted with some alternatives that need to be evaluated with respect to multiple conflicting criteria. Multi-criteria decision-making (MCDM) refers to making decisions in such a situation. There are many methods and techniques available for solving MCDM problems. The evaluation based on distance from average solution (EDAS) method is an efficient mu...

Estimation of distribution algorithms (EDAs) are derivativefree optimization approaches based on the successive estimation of the probability density function of the best solutions, and their subsequent sampling. It turns out that the success of EDAs in numerical optimization strongly depends on scaling of the variance. The contribution of this paper is a comparison of various adaptive and self...

Evolutionary algorithms (EAs) have becomeone of the successful optimization methods du-ring the last few decades, especially for pro-blems where smooth optimization cannot beused. This work summarises present trends inEstimation of distribution algorithms (EDAs),a recent kind of EAs, and suggests a furtherresearch direction. These algorithms evolve apopulation of...

Previous work has shown the efficacy of using Estimation of Distribution Algorithms (EDAs) to detect faults in concurrent software/systems. A promising feature of EDAs is the ability to analyse the information or model learned from any particular execution. The analysis performed can yield insights into the target problem allowing practitioners to adjust parameters of the algorithm or indeed th...

Most of estimation of distribution algorithms (EDAs) try to represent explicitly the relationship between variables with factorization techniques or with graphical models such as Bayesian networks. In this paper, we propose to use latent variable models such as Helmholtz machine and probabilistic principal component analysis for capturing the probabilistic distribution of given data. The latent...