Why it matters

Genetic algorithms tackle very hard problems. Understanding shapes evolutionary approach.

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The architecture

Population: multiple candidate solutions.

Selection: pick fit ones for reproduction.

Crossover + mutation: create new candidates.

Genetic algorithm opsSelectionfit → reproduceCrossovercombine parentsMutationrandom changePopulation diversity vs convergence trade-off
GA operations.
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How it works end to end

Fitness: problem-specific metric.

Elitism: keep best individuals.

Applications: engineering design, scheduling, neural architecture search.

Neuroevolution: evolve neural network structures.