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An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA.
Online Bagging for Anytime Transfer Learning
In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.
Solving dynamic multi-objective optimization problems via support vector machine
The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
