Themsen/SDR2/Notes

Notes

M. Abbasi, M. S. A. Latiff, H. Chizari, "Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks”

[1]

Furthermore, sensors also can sense the environment behind the movement, compute the data, and send the collected data to the sink node that can route the data to the other analysing centre through the internet [1].

[…] environment monitoring surveillance in military, wildlife monitoring, […]

[…] in many places that are hostile, manual deployment is impossible and nodes have to be deployed randomly [3,4].

When network size is large and sensor field is hostile, the only choice for deployment of nodes is to scatter with aircraft.

[…] mobile sensor node has great impact on network coverage. They are equipped with vehicle and move around the area after random deployment to enhance network coverage. However, mobile sensor node is very expensive in comparison to the stationary node.

[2]

According to the monitoring area, three types of coverage have been identified: area coverage, target coverage, and barrier coverage.

References

X. S. Yang, Z. H. Cui, R. B. Xiao, A. H. Gandomi, and M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, Waltham, Mass, USA, 2013.

Lawrence, Handbook of Genetic Algorithms., Van Nostr. and Reinhold, New York, NY, USA, 1996

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P. Alsina, R. Renno, ON CREATING LIFE AND DISCOURSES ABOUT LIFE: PESTS, MONSTERS, AND BIOTECHNOLOGY CHIMERAS

[1]

While genetic studies appear to be the mythical guise of pure science and objective knowledge about nature, they turn out underneath, to be political, economic and social ideology

The term Mother Nature is quite appropriate if we think that man has a tortuous relationship with her, between fear and admiration, the desire to control and to nearly destroy. Such binary visions of and often paradoxical relationships between man and nature apply to the technologies in the sciences. It is still difficult to overcome the widespread dualistic perception about technology.

[2]

the opposition between nature and culture, between the natural and artificial, between the living and the dead. As these boundaries become blurred, new issues emerge. For example, there is an economic interest in the chain of life (due to the development of biotechnologies), and in virtual environments (with development of the World Wide Web)

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T. Rowley, A Toolkit for Visual Genetic Programming, 1994

Genetic programming is an area of research in computer science directed towards finding “optimal” solutions in a large search space [Gol89].

M. Lewis, Evolutionary Visual Art and Design

[8]

The majority of expression-based image generation systems in the spirit of Sims use a reduced set of mathematical functions and often only local information for determining pixel colour. In different systems it is often possible to recognise, in the images produced, emphasised reliance on specific techniques such as fractals, polar coordinate mappings, noise functions, etc.

[9]

Hewgill and Ross focused on obtaining textures based on sampled texture data [39].

[10]

Electric Sheep project by Draves [43, 33] [...]

[…] is likely the most widely used evolutionary design project to date. The genes consist of approximately 160 parameters (Fig, 1.7b).

Rowley’s Toolkit for Visual Genetic Programming” [49] and Jourdan’s Kandid [28] provide generic frameworks capable of evolving fractal imagery (Fig 1.7a).

[11]

[…] neural networks for fitness evaluation with the goal of automatically generating interesting images [21, 54, 55].

[12]

Aupetit et al. use an interactive genetic algorithm (IGA) to evolve parameters for ant paintings [71] (see Chap. 11).

[13]

Lewis used cartoon face evolution as one domain when developing the interactive evolutionary design platform “Metavolve” within a commercial 3D animation environment (Fig 1.9) [83].

observing that evolution yielded better results for creative exploration while direct manipulation was easier given a targeted design task [84].

breeding different typefaces with an emphasis on drag-and-drop mating (Fig 1.10) [85].

References

D. Bacon (2003), geneticArt IV
A., Davidson (1999), Biography - Evolutionary image explorer
P., Kleiwig (2006), Genetic Art
J.A, Maxwell (2006)
A. Mills (2006), Evolutionary art gallery
R. Saunders, (2006), Evol
S. Baluja, D. Pomerleau, T. Jochem (1994), Towards automated artificial evolution for computer-generated images, Connection Science, 325-354
T. Jourdan (2006), Kandid, a genetic art project, Kandid.sourceforge.net
S. Draves (2006), Electric Sheep, electricsheep.org
A. Hewgill, B.J. Ross (2004), Procedural 3D texture synthesis using genetic programming, Computers and Graphics Journal, 569-584
S. Draves (2005), The electric sheep screen-saver: A case study in aesthetic evolution
~~T. Rowley (1994), A Toolkit for Genetic Visual Programming~~
M. Kelly (1999), Evol-artists - a new breed entirely
R. Saunders, J. Gero, (2001), The digital clockwork muse: A computational model of aesthetic evolution, Proceedings of the AISB Symposium on AI and Creativity in Arts and Science
S. Aupetit, V. Boredau, N. Monmarche, M, Slimane, G. Venturini (2003), Interactive evolution of ant painting, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, 1376-1383
M. Lewis (2000), Aesthetic evolutionary design with data flow networks, Proceedings of Generative Art 2000
A. Lund (2000), Evolving the shape of things to come: A comparison of direct manipulation and interactive evolutionary design, Proceedings of Generative Art 2000
M. Schmitz (2004), genoTyp, an experiment about genetic typography, Proceedings of Generative Art 2004

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