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Variation

As explained in Grokking AI Algorithms, Second Edition

Variation is the presence of differences in inherited characteristics among individuals in a population.

Variation means that individuals in a population differ in inherited characteristics. Offspring are not perfect copies: they inherit mixtures of genes and may acquire small random changes called mutations. section 4.1

A population is therefore a cloud of variants rather than one uniform category. Variation is produced continually; many variants disappear, while others thrive because they fit their environment better. section 4.1

Peppered moths

Before industrial pollution, light-colored moths were better camouflaged and dark moths made up about 2% of the population. As surfaces darkened, light moths became easier to spot, and the dark variant rose to about 95%. The example shows how existing variation can become more or less common as conditions change. section 4.1

Figure 4.3 A simple example of reproduction and mutation
04_03

Use the reproduction-and-mutation figure to visualize how inherited differences arise and pass between generations. section 4.1

How Grokking AI Algorithms, Second Edition covers it

How deep it goes
Defines inherited differences, mutation, environmental fit, and changing frequencies, but stops short of genetic mechanisms.
Examples
Uses offspring, mutations, branching evolution, and peppered moth color changes as concrete examples.
What you can do after
Evaluate how differing inherited traits enable natural selection and inspire iterative computational search.
How it connects
Connects variation to evolution, mutation, natural selection, adaptation, and evolutionary algorithms.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.