Titlebook: Advances in Stochastic and Deterministic Global Optimization; Panos M. Pardalos,Anatoly Zhigljavsky,Julius Žilin Book 2016 Springer Intern

Geyser

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https://doi.org/10.1007/978-3-663-19701-0 nonlinear bi-objective optimization problem. A heuristic algorithm is proposed which obtains a good approximation of the Pareto front when the new facilities have to be selected from a finite set of candidates.

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Combining Interval and Probabilistic Uncertainty: What Is Computable?thms for taking uncertainty into account..Before we concentrate our efforts on designing such algorithms, it is important to make sure that such an algorithm is possible in the first place, i.e., that the corresponding problem is algorithmically computable. In this paper, we analyze the computabilit

HARP

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Understanding the Impact of Constraints: A Rank Based Fitness Function for Evolutionary Methodseparate objective. The results presented show that the fitness function provides the desired selection pressure and, when combined with the multi-objective plant propagation algorithm, is able to find good designs that achieve the desired constraint simultaneously.

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Advances in Stochastic and Deterministic Global Optimization

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Book 2016objective function, the development and implementation of efficient algorithms for global optimization with single and multiple objectives, and application of algorithms for solving real-world practical problems..

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Christian Liebchen,Rolf H. Möhringthms for taking uncertainty into account..Before we concentrate our efforts on designing such algorithms, it is important to make sure that such an algorithm is possible in the first place, i.e., that the corresponding problem is algorithmically computable. In this paper, we analyze the computabilit