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Algoritma Genetik×Pengaturcaraan Integer Campuran×
BidangPengoptimumanSimulasi
KeluargaProcess / pipelineProcess / pipeline
Tahun asal19751958–1960
PengasasJohn Henry HollandRalph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
JenisPopulation-based metaheuristicMathematical optimization
Sumber perintisHolland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
AliasGA, evolutionary algorithm, Genetik Algoritma — Evrimsel OptimizasyonMIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Berkaitan56
RingkasanA 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.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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ScholarGateBandingkan kaedah: Genetic Algorithm · Mixed-Integer Programming. Dicapai 2026-06-15 daripada https://scholargate.app/ms/compare