An introduction and survey of estimation of distribution algorithms
M Hauschild, M Pelikan - Swarm and evolutionary computation, 2011 - Elsevier
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that
explore the space of potential solutions by building and sampling explicit probabilistic …
explore the space of potential solutions by building and sampling explicit probabilistic …
A survey on recent progress in the theory of evolutionary algorithms for discrete optimization
The theory of evolutionary computation for discrete search spaces has made significant
progress since the early 2010s. This survey summarizes some of the most important recent …
progress since the early 2010s. This survey summarizes some of the most important recent …
Level-based analysis of genetic algorithms and other search processes
Understanding how the time complexity of evolutionary algorithms (EAs) depend on their
parameter settings and characteristics of fitness landscapes is a fundamental problem in …
parameter settings and characteristics of fitness landscapes is a fundamental problem in …
A roadmap for solving optimization problems with estimation of distribution algorithms
In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much
popularity in the evolutionary computation community for solving optimization problems …
popularity in the evolutionary computation community for solving optimization problems …
Quantum-inspired evolutionary algorithm: A multimodel EDA
MD Platel, S Schliebs… - IEEE Transactions on …, 2008 - ieeexplore.ieee.org
The quantum-inspired evolutionary algorithm (QEA) applies several quantum computing
principles to solve optimization problems. In QEA, a population of probabilistic models of …
principles to solve optimization problems. In QEA, a population of probabilistic models of …
Estimation of distribution algorithms
Estimation of distribution algorithms (EDA s) guide the search for the optimum by building
and sampling explicit probabilistic models of promising candidate solutions. However, EDA …
and sampling explicit probabilistic models of promising candidate solutions. However, EDA …
Theory of estimation-of-distribution algorithms
Estimation-of-distribution algorithms (EDAs) are general metaheuristics used in optimization
that represent a more recent alternative to classical approaches such as evolutionary …
that represent a more recent alternative to classical approaches such as evolutionary …
Level-based analysis of the univariate marginal distribution algorithm
Abstract Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for
optimal solutions by learning and sampling from probabilistic models. Despite their …
optimal solutions by learning and sampling from probabilistic models. Despite their …
Simplified runtime analysis of estimation of distribution algorithms
Estimation of distribution algorithms (EDA) are stochastic search methods that look for
optimal solutions by learning and sampling from probabilistic models. Despite their …
optimal solutions by learning and sampling from probabilistic models. Despite their …