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Reproduction

Reproduction is an evolutionary algorithm operation that generates offspring or copies from selected parent solutions through inheritance and variation.

How 2 Manning books explain it

Grokking AI Algorithms, Second Edition

Reproduction is an evolutionary algorithm operation that generates new candidate solutions from selected parent solutions.

How it is used
Uses selected parent solutions to generate varied offspring within evolutionary search.
How deep it goes
Defines reproduction and connects it to crossover and mutation, but does not detail implementation mechanics.
Examples
Uses a biological offspring analogy and a figure showing reproduction followed by mutation.
What you can do after
Understand how evolutionary algorithms create new candidate solutions from parent genetic material.

Read how Grokking AI Algorithms, Second Edition explains reproduction →

Evolutionary Deep Learning: Genetic algorithms and neural networks

The biological or computational process by which parent individuals produce offspring.

How it is used
Uses reproduction to evolve trait values toward higher fitness in a simplified evolutionary life simulation.
How deep it goes
Explains selection, trait averaging, mutation, and generation flow, while treating reproduction variants as unexplored alternatives.
Examples
Demonstrates reproduction with cells carrying traits a, b, and c in a Python-style evolution simulation.
What you can do after
Build an evolutionary generation cycle that selects parents, combines traits, mutates children, and reevaluates fitness.

Read how Evolutionary Deep Learning: Genetic algorithms and neural networks explains reproduction →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.