השוואת שיטות

	אלגוריתם גנטי סטוכסטי	אלגוריתם גנטי
תחום	סימולציה	אופטימיזציה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1975	1975
הוגה השיטה	Holland, J. H.	John Henry Holland
סוג	Stochastic evolutionary metaheuristic	Population-based metaheuristic
מקור מכונן	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor. ISBN: 978-0262581110	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
כינויים	SGA, Canonical Genetic Algorithm, Simple Genetic Algorithm, Evolutionary Algorithm	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
קשורות	5	5
תקציר	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.	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.