Σύγκριση μεθόδων

	Γενετικός Αλγόριθμος	Υπερ-ευριστικοί (Hyper-heuristics)
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1975	2013
Δημιουργός	John Henry Holland	Burke et al.
Τύπος	Population-based metaheuristic	High-level search methodology
Θεμελιώδης πηγή	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	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.
Εναλλακτικές ονομασίες	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel
Συναφείς	5	3
Σύνοψη	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.	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.