6 citations · 9 across the 5 of their papers we have counts for
6 papers
Multiobjective Evolutionary Component Effect on Algorithm behavior
Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa +1
The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simpli…
Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition
Yuri Lavinas, Marcelo Ladeira, Claus Aranha
The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on…
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
Felipe Vaz, Yuri Lavinas, Claus Aranha +1
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints. Thus, most of the works in…
MOEA/D with Random Partial Update Strategy
Yuri Lavinas, Claus Aranha, Marcelo Ladeira +1
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can con…
Transfer Learning for Brain Tumor Segmentation
Jonas Wacker, Marcelo Ladeira, José Eduardo Vaz Nascimento
Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segm…
Classification of EEG Signals using Genetic Programming for Feature Construction
Icaro Marcelino Miranda, Claus Aranha, Marcelo Ladeira
The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy,…