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Deterministički genetski algoritam×Genetički algoritam×
PodručjeSimulacijaOptimizacija
ObiteljProcess / pipelineProcess / pipeline
Godina nastanka1975–19891975
TvoracGoldberg, D. E.; Holland, J. H.John Henry Holland
VrstaDeterministic evolutionary optimizationPopulation-based metaheuristic
Temeljni izvorGoldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA. ISBN: 9780201157673Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗
Drugi naziviDGA, Deterministic EA, Deterministic Evolutionary Algorithm, Deterministic Selection GAGA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Srodne55
SažetakA 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.
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ScholarGateUsporedite metode: Deterministic Genetic Algorithm · Genetic Algorithm. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare