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Found 6 papers, 2 papers with code
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization
These results strongly support the conclusion that the cone-eps-MOEA is a competitive approach for obtaining an efficient balance between convergence and diversity to the Pareto front, and as such represents a useful tool for the solution of multiobjective optimization problems.
MOEA/D with Random Partial Update Strategy
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 consistently improve the performance of that algorithm.
Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances
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 of interest.
Tuning metaheuristics by sequential optimization of regression models
The results suggest that the proposed approach returns solutions that are as good as those of Irace in terms of mean performance, with the advantage of providing more information on the relevance and effect of each parameter on the expected performance of the algorithm.
Sample size estimation for power and accuracy in the experimental comparison of algorithms
In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class.
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework.
