By Tim Dwyer, Helen Purchase, Aidan Delaney
This ebook constitutes the refereed court cases of the eighth foreign convention at the thought and alertness of Diagrams, Diagrams 2014, held in Melbourne, VIC, Australia in July/August 2014. The 15 revised complete papers and nine brief papers awarded including 6 posters have been conscientiously reviewed and chosen from forty submissions. The papers were prepared within the following topical sections: diagram format, diagram notations, diagramming instruments, diagrams in schooling, empirical stories and common sense and diagrams.
Read or Download Diagrammatic Representation and Inference: 8th International Conference, Diagrams 2014, Melbourne, VIC, Australia, July 28 – August 1, 2014. Proceedings PDF
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Extra info for Diagrammatic Representation and Inference: 8th International Conference, Diagrams 2014, Melbourne, VIC, Australia, July 28 – August 1, 2014. Proceedings
Von Hanxleden are combined in a process called co-evolution: the results of the weights evolution are used for the ﬁtness function of the layout evolution, while the ﬁtness of the weights is determined based on user ratings of sample layouts. We have experimented with two much simpler methods, both of which involve the user: (a) the user directly manipulates the metric weights with sliders allowing values between 0 and 1, and (b) the user selects good layouts from the current population and the metric weights are automatically adjusted according to the selection.
The line on top shows the ﬁtness values of the best genomes for iterations 0 to 6 (horizontal axis), while the bars show the fractions of genomes that are set to force-type algorithms. 5 GHz CPU. 6 new genomes were created on average. This means that about 23 layout algorithm executions had to be performed for the evaluation operation of each evolution cycle, and each layout metric has been evaluated just as often. We measured the average execution time of one evolution cycle (i. e. a single iteration), which led to the results shown in Fig.
Graph layout using a genetic algorithm. In: Proc. of the 6th Brazilian Symposium on Neural Networks, pp. 179–184 (2000) 17. : Improving graph drawing readability by incorporating readability metrics: A software tool for network analysts. Tech. Rep. HCIL-2009-13, University of Maryland (2009) 18. : Evolutionary meta layout of graphs. Technical Report 1401, Christian-Albrechts-Universit¨ at zu Kiel, Department of Computer Science (January 2014) ISSN 2192-6247 19. : Evolutionary learning of graph layout constraints from examples.