Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming

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

@Article{Pak-Kan_Wong:EC,

• author = "Pak-Kan Wong and Man-Leung Wong and Kwong-Sak Leung",
• title = "Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming",
• journal = "Evolutionary Computation",
• year = "2021",
• volume = "29",
• number = "2",
• pages = "239--268",
• month = "Summer",
• keywords = "genetic algorithms, genetic programming, adaptive grammar-based genetic programming, Estimation of distribution programming, Bayesian network classifier, data mining.",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/evco_a_00280",
• size = "30 pages",
• abstract = "Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create sub-optimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This paper presents Grammar-based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.",
• notes = "Department of Computer Science and Engineering, The Chinese University of HongKong, Hong Kong",

}

Genetic Programming entries for Pak-Kan Wong Man Leung Wong Kwong-Sak Leung

Citations