Σύγκριση μεθόδων

	Γενετικός Αλγόριθμος Ντετερμινιστικού Τύπου	Στοχαστικός Γενετικός Αλγόριθμος
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1975–1989	1975
Δημιουργός	Goldberg, D. E.; Holland, J. H.	Holland, J. H.
Τύπος	Deterministic evolutionary optimization	Stochastic evolutionary metaheuristic
Θεμελιώδης πηγή	Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110
Εναλλακτικές ονομασίες	DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm
Συναφείς	5	5
Σύνοψη	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.	The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across engineering, scheduling, machine learning, and operations research.