nical Abstract Optimization is to find the "best" solution to a problem where the quality of a solution can be measured by a given criterion. Estimation of Distribution A...
This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzm...
One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consu...
This paper benchmarks an Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimizer (PSO) on noisefree BBOB 2009 testbed. The algorithm is referred to as EDA-PSO and ...
The restarted estimation of distribution algorithm (EDA) with Cauchy distribution as the probabilistic model is tested on the BBOB 2009 testbed. These tests prove that when using ...
Estimation of distribution algorithms replace the typical crossover and mutation operators by constructing a probabilistic model and generating offspring according to this model....
— Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimizat...
Marcus Gallagher, Ian Wood, Jonathan M. Keith, Geo...
— Estimation of Distribution Algorithm (EDA) is a well-known stochastic optimization technique. The average time complexity is a crucial criterion that measures the performance o...
—Many distribution algorithms have been proposed up to now for P2P real time streaming. However, due to the lack of basic theoretical results and bounds, common sense and intuiti...
Lorenzo Bracciale, Francesca Lo Piccolo, Dario Luz...
—Despite the wide-spread popularity of estimation of distribution algorithms (EDAs), there has been no theoretical proof that there exist optimisation problems where EDAs perform...
Tianshi Chen, Per Kristian Lehre, Ke Tang, Xin Yao