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Development of a linear genetic programming algorithm using an estimation of distribution algorithm applied to supervised machine learning

Grant number: 18/13202-4
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): September 03, 2018
Effective date (End): September 02, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Márcio Porto Basgalupp
Grantee:Léo Françoso Dal Piccol Sotto
Supervisor abroad: Franz Rothlauf
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Local de pesquisa : Johannes Gutenberg University Mainz (JGU), Germany  
Associated to the scholarship:16/07095-5 - Development of the probabilistic linear genetic programming technique and application on Kaizen programming for supervised machine learning, BP.DD


Linear Genetic Programming (LGP) is a kind of algorithm capable of evolving computer code which has been successfully applied in various problems, such as Machine Learning (ML), navigation, and routing. As in other Evolutionary Algorithms (EAs), its stochastic search process neither has the knowledge to produce good quality solutions nor is able to avoid poor quality solutions, which reduces its efficacy. Furthermore, their recombination operators often ignore the correlation between the different positions in a genotype. In order to deal with this problem in EAs, researchers proposed the Estimation of Distribution Algorithm (EDA) that uses a probability model, built from good quality solutions, to sample promising solutions, which in turn are used to update the model. In order to lessen the issue with the disruptive recombination operators, bivariate and multivariate models also explore the correlation between the different string positions. The PhD project associated with this proposal has the objective of studying and proposing different probability models for LGP: univariate, bivariate, and multivariate. The resulting Probabilistic Linear Genetic Programming (PLGP) techniques will be applied on supervised ML problems, such as regression and classification. During the research stay period, the student will develop a multivariate model for LGP. The original aspect of the work is either the adaptation of existing multivariate models or the implementation of new models specifically suited to the linear structure of LGP, while its main relevance is the application on supervised ML problems. (AU)