방법 비교

	뻐꾸기 탐색	차등 진화	유전 알고리즘
분야	최적화	최적화	최적화
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	2009	1997	1975
창시자	—	Rainer Storn & Kenneth Price	John Henry Holland
유형	Population-based metaheuristic / swarm intelligence	Population-based stochastic metaheuristic	Population-based metaheuristic
원전	Yang, X.S. & Deb, S. (2009). Cuckoo Search via Lévy Flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214. IEEE.	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
별칭	Guguk Kuşu Araması (Cuckoo Search), CS algorithm, Cuckoo Search via Lévy Flights	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
관련	6	5	5
요약	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.	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.