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An introduction and survey of estimation of distribution algorithms
Swarm and Evolutionary Computation, 2011Abstract 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 probabilistic models in optimization offers some significant advantages over other types of ...
Mark Hauschild, Martin Pelikan
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Variable Transformations in Estimation of Distribution Algorithms
2012In this paper we address model selection in Estimation of Distribution Algorithms (EDAs) based on variables trasformations. Instead of the classic approach based on the choice of a statistical model able to represent the interactions among the variables in the problem, we propose to learn a transformation of the variables before the estimation of the ...
CUCCI, DAVIDE ANTONIO +2 more
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Design of Multithreaded Estimation of Distribution Algorithms
2003Estimation of Distribution Algorithms (EDAs) use a probabilistic model of promising solutions found so far to obtain new candidate solutions of an optimization problem. This paper focuses on the design of parallel EDAs. More specifically, the paper describes a method for parallel construction of Bayesian networks with local structures in form of ...
Jiri Ocenasek +2 more
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Distributed algorithm for SDP state estimation
2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2013Smart Grid State Estimation (SE) aims at providing robust and accurate system state estimate for subsequent control operations to accommodate the disturbance of highly intermittent components. Conventional SE for AC Power Grid formulates the estimation process as a non-convex optimization problem, which may reach a local optimal and stop. To compensate
Yang Weng +3 more
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Continuous Gaussian Estimation of Distribution Algorithm
2013Metaheuristics algorithms such as Estimation of Distribution Algorithms use probabilistic modeling to generate candidate solutions in optimization problems. The probabilistic presentation and modeling allows the algorithms to climb the hills in the search space.
Shahram Shahraki +1 more
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On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation, 2004We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements.
Qingfu Zhang 0001, Heinz Mühlenbein
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Using Copulas in Estimation of Distribution Algorithms
2009A new way of modeling probabilistic dependencies in Estimation of Distribution Algorithm (EDAs) is presented. By means of copulas it is possible to separate the structure of dependence from marginal distributions in a joint distribution. The use of copulas as a mechanism for modeling joint distributions and its application to EDAs is illustrated on ...
Rogelio Salinas-Gutiérrez +2 more
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Estimation of Distribution Algorithms for the Firefighter Problem
2017The firefighter problem is a graph-based optimization problem in which the goal is to effectively prevent the spread of a threat in a graph using a limited supply of resources. Recently, metaheuristic approaches to this problem have been proposed, including ant colony optimization and evolutionary algorithms.
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Customized Selection in Estimation of Distribution Algorithms
2014Selection plays an important role in estimation of distribution algorithms. It determines the solutions that will be modeled to represent the promising areas of the search space. There is a strong relationship between the strength of selection and the type and number of dependencies that are captured by the models.
Roberto Santana 0001 +2 more
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Reinforcement Learning Estimation of Distribution Algorithm
2003This paper proposes an algorithm for combinatorial optimizations that uses reinforcement learning and estimation of joint probability distribution of promising solutions to generate a new population of solutions. We call it Reinforcement Learning Estimation of Distribution Algorithm (RELEDA).
Topon Kumar Paul, Hitoshi Iba
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