Σύγκριση μεθόδων

	Cuckoo Search	Γενετικός Αλγόριθμος
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2009	1975
Δημιουργός	—	John Henry Holland
Τύπος	Population-based metaheuristic / swarm intelligence	Population-based metaheuristic
Θεμελιώδης πηγή	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
Εναλλακτικές ονομασίες	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
Συναφείς	6	5
Σύνοψη	Cuckoo Search (CS) is a population-based metaheuristic optimization algorithm introduced by Xin-She Yang and Suash Deb in 2009. It models the obligate brood-parasitism of cuckoo birds — which lay eggs in other birds' nests — combined with Lévy flight random walks that enable long-range exploration of the search space. The algorithm has proven effective in structural engineering design, machine learning hyperparameter tuning, and other continuous black-box optimization problems.	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.