3 citations · 3 across the 1 of their papers we have counts for
6 papers
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization
Lucas S. Batista, Felipe Campelo, Frederico G. Guimarães +1
We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared wit…
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…
Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances
Felipe Campelo, Elizabeth F. Wanner
This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class…
Tuning metaheuristics by sequential optimization of regression models
Áthila R. Trindade, Felipe Campelo
Tuning parameters is an important step for the application of metaheuristics to problem classes of interest. In this work we present a tuning framework based on the sequential opti…
Sample size estimation for power and accuracy in the experimental comparison of algorithms
Felipe Campelo, Fernanda Takahashi
Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample…
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
Felipe Campelo, Lucas S. Batista, Claus Aranha
Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimizati…