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Libri sul tema "Optimisation de concepts multidisciplinaires"

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Autore: Grafiati
Pubblicato: 7 luglio 2024

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1

Michael, Affenzeller, a cura di. Genetic algorithms and genetic programming: Modern concepts and practical applications. Boca Raton: Chapman & Hall/CRC, 2009.

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2

Bektas, Tolga. Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2017.

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3

Bektas, Tolga. Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2017.

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4

Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2017.

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5

Bektas, Tolga. Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2017.

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6

Bektas, Tolga. Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2017.

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7

Bektas, Tolga. Freight Transport and Distribution: Concepts and Optimisation Models. Taylor & Francis Group, 2019.

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8

Miclet, Laurent, Yves Kodratoff, Antoine Cornuéjols e Tom Mitchell. Apprentissage artificiel : Concepts et algorithmes. Eyrolles, 2002.

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9

Wagner, Stefan, Michael Affenzeller, Stephan Winkler e Andreas Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Taylor & Francis Group, 2009.

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10

Wagner, Stefan, Michael Affenzeller, Stephan Winkler e Andreas Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Taylor & Francis Group, 2018.

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11

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights). Chapman & Hall/CRC, 2008.

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12

Spatial Tessellations: Concepts and Applications of Voronoi Diagrams (Wiley Series in Probability and Statistics). 2a ed. Wiley, 2000.

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13

Schiemann, Anja, Clara Remke e Katharina Büchler, a cura di. HEADS, KURS & Co. Nomos Verlagsgesellschaft mbH & Co. KG, 2019. http://dx.doi.org/10.5771/9783845297866.

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Abstract (sommario):
The monitoring concepts of German federal states aim to protect the public from sex offenders and violent offenders at risk of recidivism, which is intended to be achieved through good cooperation and exchange of information between the agencies involved. This study evaluates these structures and the procedures within the concepts in 10 German federal states. Since those concepts are designed to minimise the risk of recidivism as a preventive measure, a process evaluation offers a good opportunity to examine the efficiency and effectiveness of the processes carried out. In a subsequent comparison, the study identifies weaknesses and mistakes in the concepts in order to submit proposals for their optimisation and initiate further research studies. With contributions by Anja Schiemann, Katharina Büchler, Inge Roith, Ilka Freyhat, Helge Risom, Thomas Heberer, Candy Sommer, Tina Beck, Sven Pahl, Christoph Dümmig, Maik Schröder, Daniel Sühling
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14

Oulasvirta, Antti, e Andreas Karrenbauer. Combinatorial Optimization for User Interface Design. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0005.

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Abstract (sommario):
Combinatorial optimization offers a rigorous but powerful approach to user interface design problems, defining problems mathematically such that they can be algorithmically solved. Design is defined as algorithmic combination of design decisions to obtain an optimal solution defined by an objective function. There are strong rationale for this method. First, core concepts such as ’design task’, ’design objective’, and ’optimal design’ become explicit and actionable. Second, solutions work well in practice, even for some problems traditionally out of reach of manual solutions. The method can assist in the generation, refinement, and adaptation of design. However, mathematical expression of HCI problems has been challenging and curbed applications. This chapter introduces combinatorial optimisation from user interface design point of view, and addresses two core challenges: 1) mathematical definition of design problems and 2) expression of evaluative knowledge such as design heuristics and predictive models of interaction.
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15

Williamson, John H., Antti Oulasvirta, Per Ola Kristensson e Nikola Banovic, a cura di. Bayesian Methods for Interaction and Design. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108874830.

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Abstract (sommario):
Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can be in thinking about human-computer interaction, especially in representing and manipulating uncertainty. Bayesian methods are increasingly practical as computational tools to implement them become more widely available, and offer a principled foundation to reason about interaction design. The book opens with a self-contained tutorial on Bayesian concepts and their practical implementation, tailored for the background and needs of interaction designers. The contributed chapters cover the use of Bayesian probabilistic modelling in a diverse set of applications, including improving pointing-based interfaces; efficient text entry using modern language models; advanced interface design using cutting-edge techniques in Bayesian optimisation; and Bayesian approaches to modelling the cognitive processes of users.
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