Stagnation

Q: How deeply does Evolutionary Deep Learning: Genetic algorithms and neural networks cover stagnation?

Defines stagnation and exposes max_stagnation as a configuration interval, without detailing the extinction procedure.

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

Stagnation is a period during which an evolutionary species shows no discernible improvement in fitness.

Stagnation is a period in which an evolutionary species shows no discernible improvement in fitness. In NEAT, stagnation helps determine when a species has remained unproductive long enough to consider extinction. section 10.5.1

Configure stagnation alongside speciation controls:

Set max_stagnation to the number of generations allowed before checking a species for extinction. section 10.5.1
Interpret stagnation with compatibility_threshold, which controls how networks are grouped into species. section 10.5.1

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

Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks

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Also explained in

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AI Agents in Action, Second EditionStagnation detection is the identification of repeated iterations whose outputs do not meaningfully change.

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Related concepts

aspiration criterionaddressed by · An aspiration criterion is a rule that permits a tabu move when it meets a specified exception condition, such as producing an especially good solution.
species extinctionproduces · Species extinction is the removal of an evolutionary species from the population when it no longer persists or remains viable.
optimal solutionuses · An optimal solution is a feasible solution that achieves the best objective value among the alternatives considered.
metacognitionpart of · Metacognition is the capacity to monitor, evaluate, or regulate one's own cognitive processes.
iteration limitcontrasts with · An iteration limit is a configured maximum number of repeated cycles or updates an iterative process may perform before stopping.

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.