How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations...

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Veröffentlicht in:	PLoS computational biology Jg. 13; H. 4; S. e1005358
Hauptverfasser:	Kouvaris, Kostas, Clune, Jeff, Kounios, Loizos, Brede, Markus, Watson, Richard A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.04.2017 Public Library of Science (PLoS)
Schlagworte:	Adaptation Amplification Artificial intelligence Automatic control Background noise Biological Evolution Biological research Biology Biology and Life Sciences Brain Careers Cerebral cortex Classification Cliffs Colleges & universities Computational Biology Computational neuroscience Computer applications Cortex Data acquisition Data collection Data processing Detectors Environment Evolution Evolution & development Evolutionary adaptation Evolutionary biology Flexibility Gene duplication Gene expression Gene regulation Genetic algorithms Genetics Genomes Humans Laboratories Learning Learning algorithms Machine Learning Mathematical analysis Mathematical models Models, Biological Modularity Monte Carlo simulation Natural selection Neural networks Noise Noise prediction Optimization Organisms Origins Pattern recognition Phenotype Phenotypic variations Physical Sciences Population genetics Populations Pressure Probability theory Ribonuclease Securities prices Selection, Genetic Simulation Social Sciences Statistical analysis Stochasticity Technology Theory Variability
ISSN:	1553-7358, 1553-734X, 1553-7358
Online-Zugang:	Volltext
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