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Found 7 papers, 4 papers with code
Multiobjective Evolutionary Component Effect on Algorithm behavior
In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems.
Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems
This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm.
Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition
MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.
Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations
Finding good solutions for Multi-objective Optimization (MOPs) Problems is considered a hard problem, especially when considering MOPs with constraints.
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.
Transfer Learning for Brain Tumor Segmentation
Therefore, there is a substantial demand for automatic image segmentation algorithms that produce a reliable and accurate segmentation of various brain tissue types.
Classification of EEG Signals using Genetic Programming for Feature Construction
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, head injuries, dizziness, headaches and brain tumors.
