A review on probabilistic graphical models in evolutionary computation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Larranaga:2012:JH,

• author = "Pedro Larranaga and Hossein Karshenas and Concha Bielza and Roberto Santana",
• title = "A review on probabilistic graphical models in evolutionary computation",
• journal = "Journal of Heuristics",
• year = "2012",
• volume = "18",
• number = "5",
• pages = "795--819",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, EDA, Probabilistic graphical model, Bayesian network, Evolutionary computation, Estimation of distribution algorithm, model based GP, PIPE, ECGP, GP-EDA",
• ISSN = "1381-1231",
• language = "English",
• URL = "http://dx.doi.org/10.1007/s10732-012-9208-4",
• DOI = "doi:10.1007/s10732-012-9208-4",
• size = "25 pages",
• abstract = "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 modelling in these algorithms.",
• notes = "Stuff on GP mostly on page 813",

}

Genetic Programming entries for Pedro Larranaga Hossein Karshenas Concha Bielza Roberto Santana

Citations