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Titlebook: EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation; Emilia Tantar,Alexandru-Adrian Tantar,Oliver Sch

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dapt the buying and selling rules of the investigated algorithms resulting in a new algorithm. Results show that a genetic programming approach does not lead to good new algorithms. We extend former works by using the . as a measure of risk, and by applying competitive analysis.
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On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithmseal-valued MOPs. We show the main aspects to be considered when building local search operators using the objective function gradients, and when coupling them with evolutionary algorithms. We present an overview of our current methods with discussion about their convenience for particular kinds of problems.
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covering of .. Further, we present a class of multiobjective optimal control problems which can be efficiently handled by the set oriented continuation methods using a transformation into high-dimensionalMOPs. We illustrate all the methods on both academic and real world examples.
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the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:
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Cooperative Coevolution for Agrifood Process Modelingthe design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:
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