Linganisha mbinu

	Hyper-heuristics	Algorithimu ya Kijenetiki
Nyanja	Uboreshaji	Uboreshaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2013	1975
Mwanzilishi	Burke et al.	John Henry Holland
Aina	High-level search methodology	Population-based metaheuristic
Chanzo asilia	Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Majina mbadala	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Zinazohusiana	3	5
Muhtasari	Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge.	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.