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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
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
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	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 that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting 'quick fixes' (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: RAW KK JC.Performed the experiments: KK.Analyzed the data: KK RAW MB LK.Wrote the paper: KK RAW JC MB LK. The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1005358