3 citations · 3 across the 8 of their papers we have counts for
8 papers
AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems
Alexandre Mascarenhas, Yuri Lavinas, Claus Aranha
Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications. The main issues to…
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…
Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems
Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa +1
The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development an…
Search Trajectories Networks of Multiobjective Evolutionary Algorithms
Yuri Lavinas, Claus Aranha, Gabriela Ochoa
Understanding the search dynamics of multiobjective evolutionary algorithms (MOEAs) is still an open problem. This paper extends a recent network-based tool, search trajectory netw…
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…
MOEA/D with Adaptative Number of Weight Vectors
Yuri Lavinas, Abe Mitsu Teru, Yuta Kobayashi +1
The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a popular algorithm for solving Multi-Objective Problems (MOPs). The main component of MOEA/D is to de…
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…