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Algorisme genètic×Programació per objectius×
CampOptimitzacióPresa de decisions
FamíliaProcess / pipelineMCDM
Any d'origen19751955
Autor originalJohn Henry HollandCharnes, A., Cooper, W. W.
TipusPopulation-based metaheuristicMulti-objective optimisation — weighted/lexicographic goal deviation minimisation
Font seminalHolland, 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 ↗
ÀliesGA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Relacionats58
ResumA 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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ScholarGateCompara mètodes: Genetic Algorithm · GOAL-PROGRAMMING. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare