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Related Concept Videos

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Evolutionary Psychology01:20

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Related Experiment Video

Updated: Jun 13, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Generalization as the great leap in evolvability: insights from machine learning.

Steven A Frank1

  • 1Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697-2525, USA.

Evolution; International Journal of Organic Evolution
|June 11, 2026
PubMed
Summary

Natural selection learns solutions from past challenges. New research shows that larger, more complex biological systems, like those in evolution, generalize better to new situations, enhancing evolvability.

Keywords:
Genetic regulatory networksdouble descentfitness landscapeoverparameterization

Related Experiment Videos

Last Updated: Jun 13, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

  • Evolutionary biology
  • Machine learning
  • Genomics

Background:

  • Natural selection encodes learned information in the genome, but learned solutions may be specific to past environments.
  • Generalization, the ability to perform well across variations, is crucial for evolvability and adapting to novel challenges.
  • Traditional learning theory suggested limited generalization in systems with many parameters.

Purpose of the Study:

  • To explore how biological systems generalize learned information encoded in the genome.
  • To investigate the relationship between genomic complexity, parameterization, and the ability to find general solutions.
  • To apply recent machine learning insights on generalization to the process of natural selection and evolution.

Main Methods:

  • Reviewing machine learning research on generalization in large systems with numerous parameters.
  • Applying machine learning theories of generalization to the principles of natural selection as a learning algorithm.
  • Analyzing the association between regulatory complexity, genomic parameterization, and evolvability.

Main Results:

  • Contrary to traditional theory, machine learning models with more parameters demonstrate superior generalization capabilities.
  • Systems with higher parameterization and regulatory complexity exhibit increased evolvability for discovering general solutions.
  • Increased genomic complexity correlates with enhanced generalization, enabling adaptation to diverse environmental challenges.

Conclusions:

  • The principles of generalization discovered in machine learning are applicable to biological evolution.
  • Genomic complexity and parameterization are key factors driving the evolvability of general solutions through natural selection.
  • The link between genomic complexity and generalization may have been a significant evolutionary driver.