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Found 6 papers, 0 papers with code
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective functions evolve over time.
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions.
Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation
Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model.
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
In the preference elicitation session, the preference information learned in the consultation module is translated into the form that can be used in a decomposition-based EMO algorithm, i. e., a set of reference points that are biased toward to the ROI.
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously.
Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
Existing studies on dynamic multi-objective optimization focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature.
