most citedGeneral Subpopulation Framework and Taming the Conflict Inside Populations

18 citations · 31 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CY2019★ 2 cited

Tackling Unit Commitment and Load Dispatch Problems Considering All Constraints with Evolutionary Computation

Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano

Unit commitment and load dispatch problems are important and complex problems in power system operations that have being traditionally solved separately. In this paper, both proble…

cs.NE2019★ 18 cited

General Subpopulation Framework and Taming the Conflict Inside Populations

Danilo Vasconcellos Vargas, Junichi Murata, Hirotaka Takano +1

Structured evolutionary algorithms have been investigated for some time. However, they have been under-explored specially in the field of multi-objective optimization. Despite thei…

cs.NE2018

Self Organizing Classifiers and Niched Fitness

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problem…

cs.NE2018

Self Organizing Classifiers: First Steps in Structured Evolutionary Machine Learning

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with…

cs.LG2018

Contingency Training

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

When applied to high-dimensional datasets, feature selection algorithms might still leave dozens of irrelevant variables in the dataset. Therefore, even after feature selection has…

cs.AI2018★ 11 cited

Novelty-organizing team of classifiers in noisy and dynamic environments

Danilo Vasconcellos Vargas, Hirotaka Takano, Junichi Murata

In the real world, the environment is constantly changing with the input variables under the effect of noise. However, few algorithms were shown to be able to work under those circ…