Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm

Brownlee, Alexander E. I.; McCall, John; Zhang, Qingfu; Brown, Deryck F.

doi:10.1109/cec.2008.4631150

Cited by 26 publications

(40 citation statements)

References 19 publications

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“…The coefficients pointed towards application of the nematodes at points which matched the lifecycle of the fly larvae. Analysis of univariate and bivariate model coefficients for a number of further fitness functions was conducted in [4] and reinforced this finding.…”

Section: Model Quality and The Fitness Prediction Correlationmentioning

confidence: 78%

“…While the number of function evaluations to reach an optimum were significantly fewer than with other algorithms in our earlier work the time taken to build and sample the probabilistic model was large. However, it has been found that the Markov network at the heart of DEUM represents a good model of fitness [5,7], and this has been exploited in recent work [8] as well as providing an explanation for the previous good results.…”

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confidence: 99%

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The Markov Network Fitness Model

Brownlee

McCall

Shakya

2012

Adaptation, Learning, and Optimization

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Fitness modelling is an area of research which has recently received much interest among the evolutionary computing community. Fitness models can improve the efficiency of optimisation through direct sampling to generate new solutions, guiding of traditional genetic operators or as surrogates for a noisy or long-running fitness functions. In this chapter we discuss the application of Markov networks to fitness modelling of black-box functions within evolutionary computation, accompanied by discussion on the relationship between Markov networks and Walsh analysis of fitness functions. We review alternative fitness modelling and approximation techniques and draw comparisons with the Markov network approach. We discuss the applicability of Markov networks as fitness surrogates which may be used for constructing guided operators or more general hybrid algorithms. We conclude with some observations and issues which arise from work conducted in this area so far.

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Section: Model Quality and The Fitness Prediction Correlationmentioning

confidence: 78%

mentioning

confidence: 99%

The Markov Network Fitness Model

Brownlee

McCall

Shakya

2012

Adaptation, Learning, and Optimization

Self Cite

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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 unveil and extract problem information that is hidden in the original formulation of the function or optimization problem (Brownlee et al 2008).…”

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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“…Fitness inheritance modeling has been incorporated into EcGA ) and BOA to estimate the fitness value of new individuals and, consequently, reduce the total number of function evaluations consumed by the algorithm to reach the optimum. Brownlee et al (2008) and Brownlee (2009) used a Markov network-based fitness model in DEUM (Shakya 2006) to predict the fitness value of the new individuals and also compare their correlations to the true fitness values.…”

Section: Other Modeling Typesmentioning

confidence: 99%

A review on probabilistic graphical models in evolutionary computation

et al. 2012

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Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

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Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm

Cited by 26 publications

References 19 publications

The Markov Network Fitness Model

The Markov Network Fitness Model

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

A review on probabilistic graphical models in evolutionary computation

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