M. Pelikan

Publications146
h-index39
Citations7,423
Highly Influential Citations502
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BOA: the Bayesian optimization algorithm
TLDR
In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. Expand
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A Survey of Optimization by Building and Using Probabilistic Models
TLDR
This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. Expand
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The Bivariate Marginal Distribution Algorithm
The paper deals with the Bivariate Marginal Distribution Algorithm (BMDA). BMDA is an extension of the Univariate Marginal Distribution Algorithm (UMDA). It uses the pair gene dependencies in order… Expand
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Bayesian optimization algorithm: from single level to hierarchy
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An introduction and survey of estimation of distribution algorithms
TLDR
Abstract 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. Expand
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Linkage Problem, Distribution Estimation, and Bayesian Networks
TLDR
This paper proposes an algorithm that uses an estimation of the joint distribution of promising solutions in order to generate new candidate solutions. Expand
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Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms
  • M. Pelikan
  • Computer Science
  • SICE Annual Conference (IEEE Cat. No.03TH8734)
  • 15 December 2010
TLDR
This paper discusses probabilistic model-building genetic algorithms (PMBGAs), which are among the most important directions of current GEA research. Expand
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Bayesian Optimization Algorithm
TLDR
In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed to model multivariate data by Bayesian networks. Expand
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  • PDF
Hierarchical Bayesian Optimization Algorithm
TLDR
The hierarchical Bayesian optimization algorithm (hBOA) solves nearly decomposable and hierarchical optimization problems scalably by combining concepts from evolutionary computation, machine learning and statistics. Expand
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  • 15
Escaping hierarchical traps with competent genetic algorithms
TLDR
To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. Expand
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  • 14