File:Evolution-of-Adaptive-Behaviour-in-Robots-by-Means-of-Darwinian-Selection-pbio.1000292.s002.ogv
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Evolution-of-Adaptive-Behaviour-in-Robots-by-Means-of-Darwinian-Selection-pbio.1000292.s002.ogv (Ogg Theora video file, length 3 min 1 s, 400 × 300 pixels, 248 kbps, file size: 5.35 MB)
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[edit]DescriptionEvolution-of-Adaptive-Behaviour-in-Robots-by-Means-of-Darwinian-Selection-pbio.1000292.s002.ogv |
English: Method for evolving the neural network of a robot. Valid gene sequences are extracted (magnifying lens) from a binary string representing the genome of the robot. Those genes are translated into neurons of different type (colour) according to the genetic specifications, such as sensory, motor, excitatory, or inhibitory neurons. The corresponding neural network is connected to the sensors and motors of the robot and the resulting behaviour of the robot is measured according to the fitness function. The genomes of the individuals that had the worst performance are discarded from the population (symbolically thrown in a dustbin) whereas the genomes of the best individuals are paired and crossed over with small random mutations to generate new offspring (the process of selective reproduction is symbolically shown to occur in a “mother robot”). After several generations of selective reproductions with mutations, robots display better or novel behaviours.
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Source | Video S1 from Floreano D, Keller L. "Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection". PLOS Biology. DOI:10.1371/journal.pbio.1000292. PMID 20126252. PMC: 2811146. | ||
Author | Floreano D, Keller L | ||
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![]() ![]() This file is licensed under the Creative Commons Attribution 3.0 Unported license.
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 00:02, 1 September 2012 | 3 min 1 s, 400 × 300 (5.35 MB) | Open Access Media Importer Bot (talk | contribs) | Uploaded with the Open Access Media Importer. (test edit) botrequest |
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Short title | Video S1 |
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Author | Floreano D, Keller L |
Usage terms | http://creativecommons.org/licenses/by/3.0/ |
Image title | Method for evolving the neural network of a robot. Valid gene sequences are extracted (magnifying lens) from a binary string representing the genome of the robot. Those genes are translated into neurons of different type (colour) according to the genetic specifications, such as sensory, motor, excitatory, or inhibitory neurons. The corresponding neural network is connected to the sensors and motors of the robot and the resulting behaviour of the robot is measured according to the fitness function. The genomes of the individuals that had the worst performance are discarded from the population (symbolically thrown in a dustbin) whereas the genomes of the best individuals are paired and crossed over with small random mutations to generate new offspring (the process of selective reproduction is symbolically shown to occur in a ?mother robot?). After several generations of selective reproductions with mutations, robots display better or novel behaviours. (7.82 MB MOV) |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2010-01-26 |