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Genetický algoritmus×Programovanie cieľov×Programovanie s celočíselnými premennými×
OdborOptimalizáciaRozhodovanieSimulácia
RodinaProcess / pipelineMCDMProcess / pipeline
Rok vzniku197519551958–1960
TvorcaJohn Henry HollandCharnes, A., Cooper, W. W.Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
TypPopulation-based metaheuristicMulti-objective optimisation — weighted/lexicographic goal deviation minimisationMathematical optimization
Pôvodný zdrojHolland, 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 ↗Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Ďalšie názvyGA, evolutionary algorithm, Genetik Algoritma — Evrimsel OptimizasyonMIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Príbuzné586
ZhrnutieA 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.Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.
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ScholarGatePorovnať metódy: Genetic Algorithm · GOAL-PROGRAMMING · Mixed-Integer Programming. Získané 2026-06-15 z https://scholargate.app/sk/compare