Σύγκριση μεθόδων

	Γενετικός Αλγόριθμος Ντετερμινιστικού Τύπου	Γενετικός Αλγόριθμος
Πεδίο	Προσομοίωση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1975–1989	1975
Δημιουργός	Goldberg, D. E.; Holland, J. H.	John Henry Holland
Τύπος	Deterministic evolutionary optimization	Population-based 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.
Εναλλακτικές ονομασίες	DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.