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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 …
Personalized search inspired fast interactive estimation of distribution algorithm and its application
Interactive evolutionary algorithms have been applied to personalized search, in which less
user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive …
user fatigue and efficient search are pursued. Motivated by this, we present a fast interactive …
Model accuracy in the Bayesian optimization algorithm
Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not
much information available. From this standpoint, estimation of distribution algorithms …
much information available. From this standpoint, estimation of distribution algorithms …
Transfer learning, soft distance-based bias, and the hierarchical boa
An automated technique has recently been proposed to transfer learning in the hierarchical
Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique …
Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique …
Research topics in discrete estimation of distribution algorithms based on factorizations
In this paper, we identify a number of topics relevant for the improvement and development
of discrete estimation of distribution algorithms. Focusing on the role of probability …
of discrete estimation of distribution algorithms. Focusing on the role of probability …
Toward understanding EDAs based on Bayesian networks through a quantitative analysis
The successful application of estimation of distribution algorithms (EDAs) to solve different
kinds of problems has reinforced their candidature as promising black-box optimization …
kinds of problems has reinforced their candidature as promising black-box optimization …
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables
using an agglomerative hierarchical clustering algorithm and linkage trees. This enables …
using an agglomerative hierarchical clustering algorithm and linkage trees. This enables …
[PDF][PDF] Introduction to estimation of distribution algorithms
Estimation of distribution algorithms (EDAs) guide the search for the optimum by building
and sampling explicit probabilistic models of promising candidate solutions. However, EDAs …
and sampling explicit probabilistic models of promising candidate solutions. However, EDAs …
[PDF][PDF] MATEDA: A suite of EDA programs in Matlab
Abstract This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of
distribution algorithms. The package allows the optimization of single and multi-objective …
distribution algorithms. The package allows the optimization of single and multi-objective …
[PDF][PDF] Learn from the past: Improving model-directed optimization by transfer learning based on distance-based bias
For many optimization problems it is possible to define a problem-specific distance metric
over decision variables that correlates with the strength of interactions between the …
over decision variables that correlates with the strength of interactions between the …