পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মেমেটিক অ্যালগরিদম× | জেনেটিক অ্যালগরিদম× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1989 | 1975 |
| প্রবর্তক≠ | Pablo Moscato | John Henry Holland |
| ধরন≠ | Hybrid metaheuristic | Population-based metaheuristic |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম≠ | Hybrid Evolutionary Algorithm, Cultural Algorithm (local-search variant), Genetic Local Search, Memetik Algoritma | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| সম্পর্কিত≠ | 3 | 5 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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