Genealogy plot

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

A genealogy plot is a graph showing ancestry and generational connections among individuals or populations in an evolutionary process.

A genealogy plot visualizes how individuals connect across generations during evolutionary optimization. Each circle represents a generation, while arrows mark offspring relationships and connected evolutionary steps. section 3.4

Use the graph to inspect whether genetic operators are producing viable progress:

• Many arrows and connections suggest that offspring remain viable and that evolution is progressing from less fit toward more fit individuals. section 3.4
• Few or absent connections suggest that crossover is failing to produce viable offspring. section 3.4
• Isolated points may represent random offspring with good fitness but no viable descendants. section 3.4

Comparing crossover operators

Set the destination count to 10, reduce the population and generations to 5, generate the plot during evolution, and rerun the notebook after changing crossover operators. Small populations keep rendering manageable and the resulting plots support operator comparison. section 3.4

Figure 3.8 A genealogy plot showing individuals evolving using crossover

Inspect the illustrated flow of individuals evolving through crossover. section 3.4

Figure 3.9 A comparison of genealogy plots of crossover operations

Compare genealogy patterns produced by different crossover operations. section 3.4

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes

Explains node and arrow meanings, interprets connection patterns, and demonstrates comparison experiments while deferring large-population rendering.

Examples

Uses DEAP notebook plots, crossover comparisons, small populations, and evolving individuals as concrete inspection artifacts.

What it assumes

Assumes genetic algorithms, populations, generations, fitness, offspring, and crossover operators.

What you can do after

Diagnose crossover effectiveness by reading connections, isolated points, and fitness progression across generations.

How it connects

Connects crossover, genetic operators, fitness, offspring viability, population evolution, and genetic algorithms.

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Related concepts

• crossovermeasures · Crossover is an evolutionary recombination technique that combines material from parent candidate solutions to produce one or more offspring.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.