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Генетический алгоритм×Программирование целевых установок×
ОбластьОптимизацияПринятие решений
СемействоProcess / pipelineMCDM
Год появления19751955
Автор методаJohn Henry HollandCharnes, A., Cooper, W. W.
ТипPopulation-based metaheuristicMulti-objective optimisation — weighted/lexicographic goal deviation minimisation
Основополагающий источникHolland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗
Другие названияGA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Связанные58
СводкаA 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.GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateСравнение методов: Genetic Algorithm · GOAL-PROGRAMMING. Получено 2026-06-15 из https://scholargate.app/ru/compare