方法证据记录
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Deterministic Genetic Algorithm — Evolutionary optimization with deterministic selection and operators
分类方法记录 · 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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