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[Many optimization problems that have to be solved in practice are black box problems. Often, not much is known about an optimization problem except the information one can get via function evaluations. Neither derivatives nor constraints are known. In the worst case, nothing is even known about...
[Many real-world problems are multimodal, which renders an optimization problem difficult to solve. Local search methods, i.e., methods that greedily improve solutions based on search in the neighborhood of a solution, often only find an arbitrary local optimum that is not guaranteed to be the...
[Parameter control is an essential aspect of successful evolutionary search. Various parameter control and tuning methods have been proposed in the history of evolutionary computation, cf. Fig. 3.1 for a short taxonomy.]
[Constraints can make a hard optimization problem even harder. They restrict the solution space to a feasible subspace.]
[Hybridization has developed to an effective strategy in algorithm design. Hybrid algorithms can become more efficient and more effective than their native counterparts]
[In many design process scenarios, the optimization of more than one objective at once is a frequent problem. This setting is a particularly difficult task, when the optimization objectives are conflictive, i.e., minimization of one objective potentially results in maximization of another.]
[In supervised learning scenarios, the objective is to learn a functional model f that best explains a set of observed patterns with their corresponding labels.]
[In big data scenarios, large numbers of high-dimensional patterns have to be processed. Efficient dimensionality reduction (DR) methods are required for algorithms that can only handle low-dimensional data like weak classifiers.]
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