Generalization and Applications of Interaction-Transformation Algebraic Data Type

• Grant number: 18/14173-8
• Support Opportunities: Regular Research Grants
• Start date: December 01, 2018
• End date: November 30, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Fabricio Olivetti de França
• Grantee: Fabricio Olivetti de França
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil

Abstract

The Interaction-Transformation Algebraic Data Type is a novel representation for symbolic regression problems that restricts the search space to simple and valid expressions. It was created to be a mid-term between the simplicity of linear regression models and the approximation power of black box models. This project has the objective of generalizing this representation to other applications, expanding its search space and employ it into real-world applications. (AU)

Scientific publications

(7)

(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)

DE FRANCA, FABRICIO OLIVETTI; DE LIMA, MAIRA ZABUSCHA. Interaction-transformation symbolic regression with extreme learning machine. Neurocomputing, v. 423, p. 609-619, JAN 29 2021. (18/14173-8)

IMAI ALDEIA, GUILHERME SEIDYO; DE FRANCA, FABRICIO OLIVETTI; ACM. Measuring Feature Importance of Symbolic Regression Models Using Partial Effects. PROCEEDINGS OF THE 2021 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'21), v. N/A, p. 9-pg., 2021-01-01. (18/14173-8)

IMAI ALDEIA, GUILHERME SEIDYO; DE FRANCA, FABRICIO OLIVETTI. Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set. Genetic Programming and Evolvable Machines, v. N/A, p. 41-pg., 2022-05-30. (18/14173-8)

IMAI ALDEIA, GUILHERME SEIDYO; DE FRANCA, FABRICIO OLIVETTI; IEEE. A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression. 2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), v. N/A, p. 8-pg., 2020-01-01. (18/14173-8)

KANTOR, DANIEL; VON ZUBEN, FERNANDO J.; DE FRANCA, FABRICIO OLIVETTI; ACM. Simulated Annealing for Symbolic Regression. PROCEEDINGS OF THE 2021 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'21), v. N/A, p. 8-pg., 2021-01-01. (18/14173-8)

DE FRANCA, F. O.; ALDEIA, I, G. S.. Interaction-Transformation Evolutionary Algorithm for Symbolic Regression. EVOLUTIONARY COMPUTATION, v. 29, n. 3, p. 367-390, SEP 1 2021. (18/14173-8)

KRONBERGER, G.; DE FRANCA, F. O.; BURLACU, B.; HAIDER, C.; KOMMENDA, M.. hape-Constrained Symbolic Regression-Improving Extrapolation with Prior Knowledg. EVOLUTIONARY COMPUTATION, v. 30, n. 1, p. 75-98, MAR 1 2022. (18/14173-8)