方法对比
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| 模因算法× | 超启发式算法× | |
|---|---|---|
| 领域 | 优化 | 优化 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1989 | 2013 |
| 提出者≠ | Pablo Moscato | Burke et al. |
| 类型≠ | Hybrid metaheuristic | High-level search methodology |
| 开创性文献≠ | Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826. link ↗ | 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. DOI ↗ |
| 别名 | 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 |
| 相关 | 3 | 3 |
| 摘要≠ | 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. |
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