Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Algoritm memetic× | Algoritm Genetic× | |
|---|---|---|
| Domeniu | Optimizare | Optimizare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1989 | 1975 |
| Autorul original≠ | Pablo Moscato | John Henry Holland |
| Tip≠ | Hybrid metaheuristic | Population-based metaheuristic |
| Sursa seminală≠ | Moscato, P. (1989). On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech Concurrent Computation Program Report 826. link ↗ | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Denumiri alternative≠ | Hybrid Evolutionary Algorithm, Cultural Algorithm (local-search variant), Genetic Local Search, Memetik Algoritma | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Înrudite≠ | 3 | 5 |
| 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. | 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. |
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