Design of gray-box evolutionary algorithms and applications

• Grant number: 21/09720-2
• Support Opportunities: Regular Research Grants
• Start date: January 01, 2022
• End date: December 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Renato Tinós
• Grantee: Renato Tinós
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

Information about the structure of the problem is available in many optimization applications. However, this information is neglected by the vast majority of Evolutionary Algorithms (EAs), making optimization a black-box process. In contrast, gray-box optimization takes advantage of the knowledge of the interaction between decision variables to guide the search. Recently, new operators and gray-box type strategies have been developed, allowing to significantly improve the performance of EAs. This project aims to develop research involving efficient EAs in two main areas. From the point of view of designing new algorithms, the main objective is the development of recombination and perturbation operators that use the variable interaction graph to generate new solutions. From the application point of view, efficient EAs will be applied to problems involving Machine Learning in Medicine. (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)

DEL LAMA, RAFAEL SILVA; CANDIDO, RAQUEL MARIANA; CHIARI-CORREIA, NATALIA SANTANA; NOGUEIRA-BARBOSA, MARCELLO HENRIQUE; DE AZEVEDO-MARQUES, PAULO MAZZONCINI; TINOS, RENATO. Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics. JOURNAL OF DIGITAL IMAGING, v. 35, n. 3, p. 13-pg., 2022-02-07. (19/07665-4, 19/01219-2, 21/09720-2)

PRZEWOZNICZEK, MICHAL W.; TINOS, RENATO; KOMARNICKI, MARCIN M.; PAQUETE, L. First Improvement Hill Climber with Linkage Learning - on Introducing Dark Gray-Box Optimization into Statistical Linkage Learning Genetic Algorithms. PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023, v. N/A, p. 9-pg., 2023-01-01. (19/07665-4, 21/09720-2)

BALDO JUNIOR, SERGIO; CARNEIRO, MURILLO G.; DESTRO-FILHO, JOAO-BATISTA; ZHAO, LIANG; TINOS, RENATO; IEEE. Classification of coma etiology using convolutional neural networks and long-short term memory networks. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, v. N/A, p. 8-pg., 2023-01-01. (19/07665-4, 21/09720-2)

CHICANO, FRANCISCO; OCHOA, GABRIELA; WHITLEY, L. DARRELL; TINOS, RENATO. Dynastic Potential Crossover Operator. EVOLUTIONARY COMPUTATION, v. 30, n. 3, p. 38-pg., 2022-09-01. (21/09720-2, 19/07665-4)

PRZEWOZNICZEK, MICHAL W.; TINOS, RENATO; FREJ, BARTOSZ; KOMARNICKI, MARCIN M.; FIELDSEND, JE. On turning Black- into Dark Gray-optimization with the Direct Empirical Linkage Discovery and Partition Crossover. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22), v. N/A, p. 9-pg., 2022-01-01. (21/09720-2, 19/07665-4)

TINOS, RENATO; PRZEWOZNICZEK, MICHAL W.; WHITLEY, DARRELL; CHICANO, FRANCISCO; PAQUETE, L. Genetic Algorithm with Linkage Learning. PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023, v. N/A, p. 9-pg., 2023-01-01. (19/07665-4, 21/09720-2)