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Deterministic Genetic Algorithm

A Deterministic Genetic Algorithm (DGA) applies the structural framework of evolutionary computation — population, selection, crossover, and replacement — using entirely deterministic operators and fixed decision rules instead of stochastic sampling. By eliminating randomness, the algorithm becomes fully reproducible: running it twice on the same problem yields identical solutions, making it tractable for rigorous benchmarking, reproducibility studies, and systems where stochasticity is undesirable.

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Deterministic Genetic Algorithm — Evolutionary optimization with deterministic selection and operators

Dossier de méthode taxonomique · process-pipeline / simulation

Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. · ISBN 9780201157673
Mahfoud, S. W. (1995). Niching methods for genetic algorithms. IlliGAL Report No. 95001, University of Illinois at Urbana-Champaign. · URL

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Méthodes apparentées

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Taxonomic bucketDeterministic Particle Swarm Optimizationmachine-suggested · Relational suggestion, not evidence.Same method familyGenetic Algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMulti-objective genetic algorithmmachine-suggested · Relational suggestion, not evidence.Same method familySimulated Annealingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketStochastic Genetic Algorithmmachine-suggested · Relational suggestion, not evidence.