Using a Markov network model in a univariate EDA

Many problems within the biological sciences, such as DNA sequencing, protein structure prediction, and molecular docking, are being approached computationally. These problems require sophisticated solution methods that understand the complex natures of biological domains. Traditionally, such solution methods are problem specific, but recent advances in generic problem-solvers furnish hope for a new breed of computational tools. The challenge is to develop methods that can automatically learn or acquire an understanding of a complex problem domain.Estimation of Distribution Algorithms (EDAs) are generic search methods that use statistical models to learn the structure of a problem domain.EDAs have been successfully applied to many difficult search problems, such as circuit design, optimizing Ising spin glasses, and various scheduling tasks. Computational biology is a computational science that promises tangible, life-altering benefits. Research in fields such as genomics, protein structure prediction, and molecular docking, finds application in pharmacology and gene therapy. Consequently, day-to-day medicine and health have been improved, with the greatest breakthroughs still to come. These fields directly depend on advances in computer hardware and software, and there is great demand for effective computational problem-solving methods.Research in computational biology produces computational models of real-world objects or phenomena, such as molecular interactions or phylogenetic trees. The computational models can be studied, adjusted, and experimented with to generate predictions about their real-world counterparts. In 2 turn, real-world experiments help form the basis for new computational models. There is an interplay between real-world and computational research; each informs and refines the other. While a computational formulation of real-world problems is useful and convenient, there are drawbacks.The key difficulty of many computational problems, such as DNA sequence alignment, selecting key attributes for data mining, or optimizing antenna design, is that no analytic solution methods exist. There are also problems that do have analytic solutions, but those methods are too computationally expensive to be practical. Without the ability to construct an ideal solution efficiently, we are often reduced to a procedure of guess-and-check.Such a procedure, formally known as search, tries to identity the best solution(s) among a set of candidates by systematically evaluating alternatives.Search algorithms are, in many cases, a relatively easy and effective means of producing near-optimal solutions to difficult problems. However, in complex domains search is often inefficient, taking too long to be practical. This dissertation develops a search method that handles complexity better by statistically modeling the problem domain. ApproachFortunately, even when analytical methods are not available, for many computational problems it is relatively easy to test the "correctness" of a potential solution. For example, t...

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“…EDAs [24,60,63]. In particular, Markov random fields (MRFs) have been shown to be a promising basis for EDA models.…”

Section: Several Researchers Have Proposed Using Undirected Graph Modmentioning

confidence: 99%

MARLEDA: Effective distribution estimation through Markov random fields

Miikkulainen

Alden

2016

Theoretical Computer Science

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“…This research trend has received increasing attention in the field of EDAs (Brownlee et al 2008;Sastry et al 2006;Shakya et al 2005).…”

Section: Function Approximation Modulementioning

confidence: 99%

“…To create surrogate functions that help diminish the number of evaluations for costly functions Shakya et al 2005).…”

Section: Function Approximation Modulementioning

confidence: 99%

Mateda-2.0: AMATLABPackage for the Implementation and Analysis of Estimation of Distribution Algorithms

Santana¹,

Bielza²,

Larrañaga³

et al. 2010

J. Stat. Soft.

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This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

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“…DEUM and DEUM d were presented as novel univariate EDAs using a univariate model of probability distribution. They were shown to perform better than other EDAs of their type over a wide range of optimization problems [34,32,33]. This paper extends DEUM d to incorporate a simple Metropolis method [18] and shows that for linear univariate problems the proposed univariate MRF models are very effective.…”

Section: Introductionmentioning

confidence: 84%

“…This was called Distribution Estimation Using Markov Random Field (DEUM). DEUM was later extended to DEUM d , which is Distribution Estimation Using Markov Random Field with direct sampling [32,33]. DEUM and DEUM d were presented as novel univariate EDAs using a univariate model of probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Incorporating a Metropolis method in a Distribution Estimation using Markov Random Field Algorithm

Shakya

McCall

Brown

2005 IEEE Congress on Evolutionary Computation

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Using a Markov network model in a univariate EDA

Cited by 34 publications

References 8 publications

MARLEDA: Effective distribution estimation through Markov random fields

MARLEDA: Effective distribution estimation through Markov random fields

Mateda-2.0: AMATLABPackage for the Implementation and Analysis of Estimation of Distribution Algorithms

Incorporating a Metropolis method in a Distribution Estimation using Markov Random Field Algorithm

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