Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm

2009 ◽  
Author(s):  
María José Gacto ◽  
Rafael Alcalá ◽  
Francisco Herrera
Keyword(s):  
Evolutionary Algorithm ◽  
High Dimensional ◽  
Multi Objective ◽  
High Dimensional Regression ◽  
Regression Problems

scholarly journals Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

2021 ◽  
Author(s):  
Takumi Sonoda ◽  
Masaya Nakata
Keyword(s):  
Evolutionary Algorithm ◽  
High Dimensional ◽  
Support Vector ◽  
Computationally Efficient ◽  
Multi Objective ◽  
Multiple Classifiers ◽  
Training Samples ◽  
Expensive Problems ◽  
Low Dimensional ◽  
Expensive Optimization

Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

scholarly journals Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

2021 ◽  
Author(s):  
Takumi Sonoda ◽  
Masaya Nakata
Keyword(s):  
Evolutionary Algorithm ◽  
High Dimensional ◽  
Support Vector ◽  
Computationally Efficient ◽  
Multi Objective ◽  
Multiple Classifiers ◽  
Training Samples ◽  
Expensive Problems ◽  
Low Dimensional ◽  
Expensive Optimization

Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

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