Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Searching for robust pareto-optimal solutions in multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, we suggest a novel problem within the context of multi objective optimization. It concerns the control of solutions' performances in multi objective spaces. The motivation for controlling these performances comes from an inspiration to improve the robustness of solutions to physical deterioration. When deterioration occurs, the solution performances degrade. In order to prevent extended degradation and loss of robustness, an active control is implemented. Naturally, in order to enable such a control, the solution (product) should have tunable parameters that would serve as the controlled variables. Optimizing the solution for such a problem means that the tunable parameters should be found and their manipulation determined. Here the optimal solutions and the controller are designed using multi and single objective evolutionary algorithms. The paper is concluded with a discussion on the high potential of the approach for research and real life applications.