Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

Santana, Roberto; Bielza, Concha; Lozano, José A.; Larrañaga, Pedro

doi:10.1145/1569901.1569963

Cited by 20 publications

(17 citation statements)

References 21 publications

(24 reference statements)

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“…The structures estimated by these EDAs can also give a better understanding of unknown problems. Several works have studied the accuracy of these structures and the information we can obtain from them (Lima et al 2007;Karshenas et al 2009;Santana et al 2009a).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

A review on probabilistic graphical models in evolutionary computation

et al. 2012

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“…There are some studies in the literature that analyze how the dependencies between variables are represented in probabilistic models [71]. But, to the best of our knowledge, the importance of the dependencies involving objectives have not been considered so far in other EDAs used for multi-objective optimization.…”

Section: Mop Structure Estimationmentioning

confidence: 99%

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Karshenas

Santana

Bielza

et al. 2014

IEEE Trans. Evol. Computat.

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Abstract-This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

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“…The third class of implementations groups programming code conceived to take advantage of the modular structure shared by most of EDAs [11,40,66]. In this approach, the EDA components (e.g.…”

Section: Approaches To Eda Implementa-tionmentioning

confidence: 99%

Estimation of distribution algorithms

Santana

2011

Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation

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This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design strategies used for their implementations are discussed. The paper also reviews different directions to improve current EDA implementations. A number of lines for further expanding the areas of application for EDAs software are proposed.

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Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

Cited by 20 publications

References 21 publications

A review on probabilistic graphical models in evolutionary computation

A review on probabilistic graphical models in evolutionary computation

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Estimation of distribution algorithms

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