So sánh phương pháp

	Thuật toán Di truyền Lai (Memetic Algorithm)	Thuật toán di truyền	Tìm kiếm Cấm kỵ
Lĩnh vực	Tối ưu hóa	Tối ưu hóa	Tối ưu hóa
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	1989	1975	1989
Người khởi xướng	Pablo Moscato	John Henry Holland	Fred Glover
Loại	Hybrid metaheuristic	Population-based metaheuristic	Local-search metaheuristic
Công trình gốc	Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Glover, F. (1989). Tabu Search — Part I. ORSA Journal on Computing, 1(3), 190–206.
Tên gọi khác	Hybrid Evolutionary Algorithm, Cultural Algorithm (local-search variant), Genetic Local Search, Memetik Algoritma	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	Tabu Araması (Tabu Search), TS, tabu metaheuristic
Liên quan	3	5	4
Tóm tắt	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.	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.	Tabu Search is a local-search metaheuristic introduced by Fred Glover in 1989 that uses a tabu list — a short-term memory of recently visited solutions — to prevent cycling and escape local optima. By explicitly forbidding moves that reverse recent decisions, the algorithm explores the search space more broadly and, through long-term memory structures such as aspiration criteria, aims to approach the global optimum even in large, complex combinatorial problems.