Сравнение на методи

	Детерминистичен генетичен алгоритъм	Детерминистична оптимизация чрез рояк от частици
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1975–1989	1995 (PSO); deterministic formulation circa 2002
Създател	Goldberg, D. E.; Holland, J. H.	Kennedy, J., Eberhart, R. (PSO); deterministic variant formalized in convergence analysis literature
Тип	Deterministic evolutionary optimization	Swarm intelligence metaheuristic — deterministic variant
Основополагащ източник	Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673	Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 — International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE.
Други названия	DGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GA	DPSO, Deterministic PSO, PSO without stochastic components, Fully Deterministic PSO
Свързани	5	6
Резюме	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.	Deterministic Particle Swarm Optimization (DPSO) removes the stochastic random coefficients from classical PSO, replacing them with fixed cognitive and social acceleration parameters. Particles move through the search space following fully predictable trajectories, enabling reproducible convergence analysis and guaranteed termination behavior in continuous and combinatorial optimization problems.