Titlebook: Evolutionary Multi-objective Optimization in Uncertain Environments; Issues and Algorithm Chi-Keong Goh,Kay Chen Tan Book 2009 Springer-Ver

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Dynamic Evolutionary Multi-objective Optimizationblems involving such dynamic systems, the fitness landscape changes to reflect the time-varying requirements of the systems. Examples of such problems can be found in the areas of control, scheduling, vehicle routing, and autonomous path planning.

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Robust Evolutionary Multi-objective Optimizationth environmental changes. In such cases, it would be desirable to find solutions that perform reasonably well within some range of change. In fact, many real-world applications involve the simultaneous optimization of several competing objectives and are susceptible to decision or environmental para

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Evolving Robust Solutions in Multi-Objective Optimizationptimization considers the effects explicitly and seeks to minimize the consequences without eliminating efficiency. Many different approaches, including Taguchi orthogonal arrays, response surface methodology, probabilistic design analysis, have been applied for robust optimization. In operational r

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