Compară metode

	Algoritm memetic	Hiper-euristici
Domeniu	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1989	2013
Autorul original	Pablo Moscato	Burke et al.
Tip	Hybrid metaheuristic	High-level search methodology
Sursa seminală	Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826.	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.
Denumiri alternative	Hybrid Evolutionary Algorithm, Cultural Algorithm (local-search variant), Genetic Local Search, Memetik Algoritma	Heuristic of Heuristics, Algorithm Selection Hyper-Heuristic, Selection Hyper-Heuristic, Hiyer-Sezgisel
Înrudite	3	3
Rezumat	A Memetic Algorithm (MA) is a population-based metaheuristic that combines the global exploration of an evolutionary algorithm with the local exploitation of individual learning procedures. Introduced by Pablo Moscato in 1989 at Caltech, MAs draw on Richard Dawkins' concept of the meme — a unit of cultural transmission — to model the idea that solutions can improve not only through crossover and mutation but also through individual refinement within each generation.	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.