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Offspring

Offspring are new candidate individuals or solutions produced from one or more parent candidates through reproduction or variation.

How 2 Manning books explain it

Optimization Algorithms: AI techniques for design, planning, and control problems

Offspring are candidate individuals generated by applying evolutionary variation operators to parent individuals.

How it is used
Generates candidate solutions for fitness evaluation and possible inclusion in later genetic-algorithm generations.
How deep it goes
Works through binary crossover and mutation, then implements a generation loop; does not develop formal analysis of offspring quality.
Examples
Uses binary ticket-price offspring with calculated profits, a duplicate candidate, and a mutated candidate.
What you can do after
Build it from scratch in Python or apply it via pymoo to generate and evaluate ticket-price candidates.

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Grokking AI Algorithms, Second Edition

Offspring are new candidate solutions generated from existing individuals by an evolutionary algorithm's reproduction operation.

How it is used
Supplies new candidate solutions for successive generations of a genetic algorithm.
How deep it goes
Explains creation through crossover and mutation, then discusses offspring count, validity, and population replacement without detailing code.
Examples
Illustrates chromosome mixing with two-point crossover and population replacement with 80 retained individuals plus 20 offspring.
What you can do after
Evaluate/understand how offspring creation and replacement choices affect diversity, solution validity, and retention of good candidates.

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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.