Development of the probabilistic linear genetic programming technique and application on Kaizen programming for supervised machine learning

• Grant number: 16/07095-5
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: June 01, 2016
• End date: August 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Márcio Porto Basgalupp
• Grantee: Léo Françoso Dal Piccol Sotto
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
• Associated scholarship(s): 18/13202-4 - Development of a linear genetic programming algorithm using an estimation of distribution algorithm applied to supervised machine learning, BE.EP.DD

Abstract

Linear Genetic Programming is a type of algorithm able to evolve code in a programming language. LGP has been applied in a wide range of problems, as machine learning, navigation, and routing. Its stochastic search process has no knowledge to generate good solutions nor is able of avoiding poor ones, which decreases its efficiency. In this project, two topics will be explored in order to tackle these problems: the manipulation of randomness and the composition of solutions from blocks of code (modularity). Regarding the first topic, studies will be made on conditional probability models, originating the Probabilistic Linear Genetic Programming (PLGP). These models will be updated throughout the generations in order to incorporate knowledge about structures that should be used or avoided by individuals. The second topic consists of expanding the study of the Kaizen Programming (KP), which is an approach that composes a complete solution from partial solutions. They can be continually improved by means of an evolutionary technique, like PLGP, that will be developed in this project. Thus, while PLGP can be independently used for solving the same problems as LGP, it will also be employed as a module for the KP, for regression and classification problems, focusing on feature construction. The use of probabilistic models in PLGP, the adding of modularity, and the use with KP are the original aspects of the work, while the most relevant aspect is the application on the automatic construction of high quality feature sets. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DAL PICCOL SOTTO, LEO FRANCOSO; DE MELO, VINICIUS VELOSO; BASGALUPP, MARCIO PORTO. -LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem. KNOWLEDGE AND INFORMATION SYSTEMS, v. 52, n. 2, p. 445-465, AUG 2017. (13/20606-0, 16/07095-5)

SOTTO, LEO FRANCOSO DAL PICCOL; KAUFMANN, PAUL; ATKINSON, TIMOTHY; KALKREUTH, ROMAN; BASGALUPP, MARCIO PORTO. Graph representations in genetic programming. Genetic Programming and Evolvable Machines, v. 22, n. 4, SI, SEP 2021. (16/07095-5)

DE MELO, VINICIUS VELOSO; FAZENDA, ALVARO LUIZ; DAL PICCOL SOTTO, LEO FRANCOSO; IACCA, GIOVANNI; CASTILLO, PA; LAREDO, JLJ; DEVEGA, FF. A MIMD Interpreter for Genetic Programming. APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2020, v. 12104, p. 14-pg., 2020-01-01. (16/07095-5)

SOTTO, LEO FRANCOSO D. P.; KAUFMANN, PAUL; ATKINSON, TIMOTHY; KALKREUTH, ROMAN; BASGALUPP, MARCIO PORTO; ASSOC COMP MACHINERY. A Study on Graph Representations for Genetic Programming. GECCO'20: PROCEEDINGS OF THE 2020 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, v. N/A, p. 9-pg., 2020-01-01. (16/07095-5)

DE MELO, VINICIUS V.; SOTTO, LEO F. D. P.; LEONARDO, MATHEUS M.; FARIA, FABIO A.. Automatic Meta-Feature Engineering for CNN Fusion in Aerial Scene Classification Task. IEEE Geoscience and Remote Sensing Letters, v. 17, n. 9, p. 1652-1656, SEPT 2020. (18/13202-4, 16/07095-5)

SOTTO, LEO FRANCOSO DAL PICCOL; ROTHLAUF, FRANZ; DE MELO, VINICIUS VELOSO; BASGALUPP, MARCIO P.. An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming. EVOLUTIONARY COMPUTATION, v. 30, n. 1, p. 24-pg., 2022-03-01. (18/13202-4, 16/07095-5)